Introduction: Apolipoprotein C-III (apoC-III) regulates triglyceride (TG) metabolism. In plasma, apoC-III exists in non-sialylated (apoC-III_0a 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-III_0a, apoC-III_0b, 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 S_i persisted after adjusting for BMI (p = 0.02). Fasting TG was correlated with the ratio of apoC-III_0a / apoC-III₂ (r = 0.47, p<0.001), apoC-III_0b / 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-III_0a (r = 0.57, p<0.001), apoC-III_0b (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-III_0a, apoC-III_0b, 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.

Objectives: Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants.

Methods: In the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15–18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 μM change) and non-rapid progressors (NRP, n = 343, 5.8 ± 20 μM change, p < 0.001 versus RP). To deal with complex multi-modal data consisting of demographic, clinical, and laboratory variables, we propose a general data-driven framework to investigate the ACT NOW dataset. In particular, we first employed a Fisher Score-based feature selection method to identify the most effective variables and then proposed a probabilistic Bayes-based learning method for the prediction. Comparison of the methods and factors was conducted using area under the receiver operating characteristic curve (AUC) analyses and Brier score.

Results: The experimental results show that the proposed learning methods performed well in identifying or predicting RP. Among the methods, the performance of Naïve Bayes was the best (AUC 0.797, Brier score 0.085) compared to multilayer perceptron (0.729, 0.086) and random forest (0.642, 0.10). The results also show that feature selection has a significant positive impact on the data prediction performance.

Conclusions: By dealing with multi-modal data, the proposed learning methods show effectiveness in predicting prediabetics at risk for rapid atherosclerosis progression. The proposed framework demonstrated utility in outcome prediction in a typical multidimensional clinical dataset with a relatively small number of subjects, extending the potential utility of machine learning approaches beyond extremely large-scale datasets.

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition.

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

Asteroids provide fundamental clues to the formation and evolution of planetesimals. Collisional models based on the depletion of the primordial main belt of asteroids predict 10–15 craters >400 km should have formed on Ceres, the largest object between Mars and Jupiter, over the last 4.55 Gyr. Likewise, an extrapolation from the asteroid Vesta would require at least 6–7 such basins. However, Ceres’ surface appears devoid of impact craters >∼280 km. Here, we show a significant depletion of cerean craters down to 100–150 km in diameter. The overall scarcity of recognizable large craters is incompatible with collisional models, even in the case of a late implantation of Ceres in the main belt, a possibility raised by the presence of ammoniated phyllosilicates. Our results indicate that a significant population of large craters has been obliterated, implying that long-wavelength topography viscously relaxed or that Ceres experienced protracted widespread resurfacing.

Background: The cytokine MIF (Macrophage Migration Inhibitory Factor) has diverse physiological roles and is present at elevated concentrations in numerous disease states. However, its molecular heterogeneity has not been previously investigated in biological samples. Mass Spectrometric Immunoassay (MSIA) may help elucidate MIF post-translational modifications existing in vivo and provide additional clarity regarding its relationship to diverse pathologies.

Results: In this work, we have developed and validated a fully quantitative MSIA assay for MIF, and used it in the discovery and quantification of different proteoforms of MIF in serum samples, including cysteinylated and glycated MIF. The MSIA assay had a linear range of 1.56-50 ng/mL, and exhibited good precision, linearity, and recovery characteristics. The new assay was applied to a small cohort of human serum samples, and benchmarked against an MIF ELISA assay.

Conclusions: The quantitative MIF MSIA assay provides a sensitive, precise and high throughput method to delineate and quantify MIF proteoforms in biological samples.

Background: Cystatin C (CysC) is an endogenous cysteine protease inhibitor that can be used to assess the progression of kidney function. Recent studies demonstrate that CysC is a more specific indicator of glomerular filtration rate (GFR) than creatinine. CysC in plasma exists in multiple proteoforms. The goal of this study was to clarify the association of native CysC, CysC missing N-terminal Serine (CysC des-S), and CysC without three N-terminal residues (CysC des-SSP) with diabetic chronic kidney disease (CKD).

Results: Using mass spectrometric immunoassay, the plasma concentrations of native CysC and the two CysC truncation proteoforms were examined in 111 individuals from three groups: 33 non-diabetic controls, 34 participants with type 2 diabetes (DM) and without CKD and 44 participants with diabetic CKD. Native CysC concentrations were 1.4 fold greater in CKD compared to DM group (p = 0.02) and 1.5 fold greater in CKD compared to the control group (p = 0.001). CysC des-S concentrations were 1.55 fold greater in CKD compared to the DM group (p = 0.002) and 1.9 fold greater in CKD compared to the control group (p = 0.0002). CysC des-SSP concentrations were 1.8 fold greater in CKD compared to the DM group (p = 0.008) and 1.52 fold greater in CKD compared to the control group (p = 0.002). In addition, the concentrations of CysC proteoforms were greater in the setting of albuminuria. The truncated CysC proteoform concentrations were associated with estimated GFR independent of native CysC concentrations.

Conclusion: Our findings demonstrate a greater amount of CysC proteoforms in diabetic CKD. We therefore suggest assessing the role of cystatin C proteoforms in the progression of CKD.

Insulin-like growth factor 1 (IGF1) is an important biomarker for the management of growth hormone disorders. Recently there has been rising interest in deploying mass spectrometric (MS) methods of detection for measuring IGF1. However, widespread clinical adoption of any MS-based IGF1 assay will require increased throughput and speed to justify the costs of analyses, and robust industrial platforms that are reproducible across laboratories. Presented here is an MS-based quantitative IGF1 assay with performance rating of >1,000 samples/day, and a capability of quantifying IGF1 point mutations and posttranslational modifications. The throughput of the IGF1 mass spectrometric immunoassay (MSIA) benefited from a simplified sample preparation step, IGF1 immunocapture in a tip format, and high-throughput MALDI-TOF MS analysis. The Limit of Detection and Limit of Quantification of the resulting assay were 1.5 μg/L and 5 μg/L, respectively, with intra- and inter-assay precision CVs of less than 10%, and good linearity and recovery characteristics. The IGF1 MSIA was benchmarked against commercially available IGF1 ELISA via Bland-Altman method comparison test, resulting in a slight positive bias of 16%. The IGF1 MSIA was employed in an optimized parallel workflow utilizing two pipetting robots and MALDI-TOF-MS instruments synced into one-hour phases of sample preparation, extraction and MSIA pipette tip elution, MS data collection, and data processing. Using this workflow, high-throughput IGF1 quantification of 1,054 human samples was achieved in approximately 9 hours. This rate of assaying is a significant improvement over existing MS-based IGF1 assays, and is on par with that of the enzyme-based immunoassays. Furthermore, a mutation was detected in ∼1% of the samples (SNP: rs17884626, creating an A→T substitution at position 67 of the IGF1), demonstrating the capability of IGF1 MSIA to detect point mutations and posttranslational modifications.