Bovine serum albumin as a molecular sensor for the discrimination of complex metabolite samples

Serum albumin concentration of donor cows as an indicator of developmental competence of oocytes

Adequate nutrition is required for maintenance of normal reproduction in cattle. Albumin, the best marker and fundamental part of nutrition, most abundant plasma protein and major component of fetal bovine serum, is the best predictor of malnourishment in South African cattle. The aim of this study was to determine if serum albumin concentrations of donor cows predict the developmental competence of oocytes, and if additional protein supplementation of the in vitro culture media improves embryo outcomes in oocytes from cows with inadequate serum albumin concentrations. Oocytes (n = 1024) were recovered from donors with inadequate (≤35.9 g/L), or adequate serum albumin concentrations (≥36.0 g/L). Four hundred and sixty oocytes originated from cows with inadequate serum albumin and 564 from cows with adequate serum albumin. Oocytes of these cohorts were randomly allocated to a control and supplemented fetal bovine serum in vitro embryo culture protocol. Multiple linear, logistic and Poisson regression analyses were performed to estimate the effects of different covariates on linear, binary and count data respectively. Mixed effects Poisson regression was performed for the number of oocytes that developed into blastocysts by the seventh day of culture. Adequate serum albumin concentration of donor cows independently resulted in 46% increased blastocyst formation in the control protocol (P = 0.02). Although fetal bovine serum supplementation of the culture protocol did not affect blastocyst formation in oocytes originating from cows with inadequate serum albumin, it independently reduced blastocyst formation by 30% in oocytes originating from cows with adequate serum albumin (P = 0.02). Other independent predictors of blastocyst outcome included higher serum urea nitrogen, lower beta (β)-hydroxybutyric acid concentrations and lower fat classification of donor cows. It is concluded that adequate serum albumin of donor cows is a significant predictor of developmental competence of oocytes, and that in vitro supplementation of fetal bovine serum does not improve developmental competence of oocytes and can lead to negative blastocyst outcomes. Further research is required to determine optimal protein supplementation for oocytes originating from inadequately nourished cows.

Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LC-MS and SWATH Methodology

Human follicular fluid (hFF) is a natural environment of oocyte maturation, and some components of hFF could be used to judge oocyte capability for fertilization and further development. In our pilot small-scale study three samples from four donors (12 samples in total) were analyzed to determine which hFF proteins/peptides could be used to differentiate individual oocytes and which are patient-specific. Ultrafiltration was used to fractionate hFF to high-molecular-weight (HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome (<10 kDa) fractions. HMW and LMW compositions were analyzed using LC-MS in SWATH data acquisition and processing methodology. In total we were able to identify 158 proteins, from which 59 were never reported before as hFF components. 55 (45 not reported before) proteins were found by analyzing LMW fraction, 67 (14 not reported before) were found by analyzing HMW fraction, and 36 were identified in both fractions of hFF. We were able to perform quantitative analysis for 72 proteins from HMW fraction of hFF. We found that concentrations of 11 proteins varied substantially among hFF samples from single donors, and those proteins are promising targets to identify biomarkers useful in oocyte quality assessment.

Protein patterning by a DNA origami framework

A spatial arrangement of proteins provides structural and functional advantages in vast technological applications as well as fundamental research. Most protein patterning procedures employ complicated, time consuming and very costly nanofabrication techniques. As an alternative route, we developed a fully biomolecular self-assembly method using DNA Origami Frames (DOF) as a template for both small and large scale protein patterning. We employed a triangular DOF (tDOF) to arrange the Bovine Serum Albumin (BSA) protein. Our in situ protein patterning strategy provides a novel, fully organic platform using a fast and low-cost surface approach with possible utilization in fundamental science and technological applications.

Investigations into the Use of a Protein Sensor Assay for Metabolite Analysis

Rapid and definitive classification of biological samples has application in industrial, agricultural, and clinical settings. Considerable effort has been given to analytical methods to address such applications over the past 50 years, with the majority of successful solutions focusing on a single molecular target. However, in many cases, a single or even a few features are insufficient for accurate characterization or classification. Serum albumin (SA) proteins are a class of cargo-carrying proteins in blood that have evolved to transport a wide variety of metabolites and peptides in mammals. These proteins have up to seven binding sites which communicate allosterically to orchestrate a complex pick-up and delivery system involving a large number of different molecules at any time. The ability of SA proteins to bind multiple molecular species in a sophisticated manner inspired the development of assays to differentiate complex biological solutions. The combination of SA and high-resolution liquid chromatography mass spectrometry (LC-MS) is showing exciting promise as a protein sensor assay (PSA) for classification of complex biological samples. In this study, the PSA has been applied to cells undergoing and recovering from mild oxidative stress. Analysis using traditional LC-MS-based metabolomics failed to differentiate samples into treatment or temporal groups, whereas samples first treated with the PSA were cleanly classified into both correct treatment and temporal groups. The success of the PSA could be attributed to selective binding of metabolites, leading to a reduction in sample complexity and a general reduction in chemical noise. Metabolites important to successful sample classification were often enriched by 100-fold or more yet displayed a wide range of affinities for SA. The end result of PSA treatment is better classification of samples with a reduction in the number of features seen overall. Together, these results demonstrate how the use of a protein-based assay before LC-MS analysis can greatly improve separation and lead to more accurate and successful tracking of the metabolic state in an organism, suggesting potential application in a wide range of fields.