1.
Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma.
Harney, DJ, Hutchison, AT, Su, Z, Hatchwell, L, Heilbronn, LK, Hocking, S, James, DE, Larance, M
Molecular & cellular proteomics : MCP. 2019;(9):1899-1915
Abstract
Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.
2.
Beta cell function in type 1 diabetes determined from clinical and fasting biochemical variables.
Wentworth, JM, Bediaga, NG, Giles, LC, Ehlers, M, Gitelman, SE, Geyer, S, Evans-Molina, C, Harrison, LC, , , ,
Diabetologia. 2019;(1):33-40
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Abstract
AIMS/HYPOTHESIS Beta cell function in type 1 diabetes is commonly assessed as the average plasma C-peptide concentration over 2 h following a mixed-meal test (CPAVE). Monitoring of disease progression and response to disease-modifying therapy would benefit from a simpler, more convenient and less costly measure. Therefore, we determined whether CPAVE could be reliably estimated from routine clinical variables. METHODS Clinical and fasting biochemical data from eight randomised therapy trials involving participants with recently diagnosed type 1 diabetes were used to develop and validate linear models to estimate CPAVE and to test their accuracy in estimating loss of beta cell function and response to immune therapy. RESULTS A model based on disease duration, BMI, insulin dose, HbA1c, fasting plasma C-peptide and fasting plasma glucose most accurately estimated loss of beta cell function (area under the receiver operating characteristic curve [AUROC] 0.89 [95% CI 0.87, 0.92]) and was superior to the commonly used insulin-dose-adjusted HbA1c (IDAA1c) measure (AUROC 0.72 [95% CI 0.68, 0.76]). Model-estimated CPAVE (CPEST) reliably identified treatment effects in randomised trials. CPEST, compared with CPAVE, required only a modest (up to 17%) increase in sample size for equivalent statistical power. CONCLUSIONS/INTERPRETATION CPEST, approximated from six variables at a single time point, accurately identifies loss of beta cell function in type 1 diabetes and is comparable to CPAVE for identifying treatment effects. CPEST could serve as a convenient and economical measure of beta cell function in the clinic and as a primary outcome measure in trials of disease-modifying therapy in type 1 diabetes.
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Time-restricted feeding influences immune responses without compromising muscle performance in older men.
Gasmi, M, Sellami, M, Denham, J, Padulo, J, Kuvacic, G, Selmi, W, Khalifa, R
Nutrition (Burbank, Los Angeles County, Calif.). 2018;:29-37
Abstract
OBJECTIVE This study examined the effect of 12 wk of time-restricted feeding (TRF) on complete blood cell counts, natural killer cells, and muscle performance in 20- and 50-year-old men. METHODS Forty active and healthy participants were randomly divided into young experimental, young control, aged experimental, and aged control group. Experimental groups participated in TRF. Before (P1) and after (P2) TRF, participants performed a maximal exercise test to quantify muscle power. Resting venous blood samples were collected for blood count calculation. RESULTS No changes were identified in muscle power in all groups after TRF (P > 0.05). At P1, red cells, hemoglobin, and hematocrit were significantly higher in young participants compared with elderly participants (P < 0.05). At P2, this age effect was not found in red cells between the young experimental group and the aged experimental group (P > 0.05). At P1, white blood cells and neutrophils were significantly higher in young participants compared with elderly participants (P < 0.05). At P2, only neutrophils decreased significantly (P < 0.05) in experimental groups without significant (P > 0.05) difference among them. Lymphocytes decreased significantly in the aged experimental group at P2 (P < 0.05), whereas NKCD16+ and NKCD56+ decreased significantly in experimental groups at P2 (P < 0.05). TRF had no effect on CD3, CD4+, and CD8+ levels (P > 0.05). CONCLUSION TRF decreases hematocrit, total white blood cells, lymphocytes, and neutrophils in young and older men. TRF may be effective in preventing inflammation by decreasing natural killer cells. As such, TRF could be a lifestyle strategy to reduce systemic low-grade inflammation and age-related chronic diseases linked to immunosenescence, without compromising physical performance.