microspheres made by crosslinking of polyvinyl alcohol with glutaraldehyde functionalized with yeast lysate

Microspheres comparable with yeast cells in size were obtained by the glutaraldehyde crosslinking of polyvinyl alcohol (PVA), whose 25 g/l water solution containing magnetite particles was dispersed in the isooctane/Span 85/Tween 85 medium. yeast cell walls were partially hydrolysed by sonication in formic acid near the boiling point. The microspheres were made targets for phagocytosis by the addition of yeast lysate to the crosslinkable PVA solution and a subsequent treatment of the freshly crosslinked microspheres with diluted yeast lysate. The microspheres were also made fluorescent when the emulsification medium saturated with fluorescent 2-aminopyrimidine derivatives was used. A protocol for phagocytosis assay using the thus modified microspheres was developed.

ProtyQuant: Comparing Label-Free Shotgun Proteomics Datasets Using Accumulated Peptide Probabilities

<p>Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from <a href="https://bitbucket.org/lababi/protyquant/">https://bitbucket.org/lababi/protyquant/</a>.</p>

ProtyQuant: Comparing Label-Free Shotgun Proteomics Datasets Using Accumulated Peptide Probabilities

<p>Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from <a href="https://bitbucket.org/lababi/protyquant/">https://bitbucket.org/lababi/protyquant/</a>.</p>

Blastomyces dermatitidis: Antibody Detection in Sera from Dogs with Blastomycosis with Yeast Lysate Antigens Produced from Human and Dog Isolates

Dogs are common hosts to the fungal organismBlastomyces dermatitidis, which causes the systemic disease blastomycosis. The goal of our study was to compare the reactivity of twoB. dermatitidisyeast lysate antigens prepared from dog isolates (ERC-2, Wisconsin; T-58, Tennessee) and two lysate antigens prepared from human isolates (B5931 and B5896, Minnesota) against 48 serum specimens from dogs with confirmed blastomycosis using the indirect enzyme-linked immunosorbent assay (ELISA). Secondarily, we used three different ELISA substrates (Ultra TMB: A, SureBlue: B, and SureBlue Reserve: C) to compare the effectiveness of each substrate. Mean absorbance values ranged from 0.446 (B) to 0.651 (C) for the B5931 antigen and from 0.393 (B) to 0.540 (C) for the ERC-2 antigen in Trial 1. In Trial 2, the absorbance values ranged from 0.628 (B) to 0.909 (A) for the B5896 antigen and from 0.828 (B) to 1.375 (C) for the T-58 antigen. In Trial 1, the lysate antigen prepared from the human isolate B5931 exhibited the highest absorbance value and in Trial 2 the lysate prepared from the dog isolate T-58 was the most reactive. The overall results thus indicated that the T-58 lysate was the optimal reagent when used to detect antibody with the Sure-Blue Reserve substrate. Our laboratory is continuing to studyB. dermatitidisantigen and substrate combinations for the reliable immunodiagnosis of blastomycosis in humans and animals.

Blastomyces dermatitidisYeast Lysate Antigen Combinations: Antibody Detection in Dogs with Blastomycosis

The systemic fungal infection, blastomycosis, which infects both humans and animals has presented a diagnostic challenge for clinicians for many years. The aim of this study was to evaluate the diagnostic sensitivity ofBlastomyces dermatitidisyeast lysate antigens with respect to antibody detection in dogs with blastomycosis. Lysate antigens were prepared fromB. dermatitidisisolates T-58 and T-66 (dogs, Tennessee) and WI-R and WI-J (dogs, Wisconsin). Based on results obtained from a preliminary comparative study, five combinations of these isolates and one individual isolate were tested against 92 serum specimens from dogs with culture-proven or histologically-confirmed blastomycosis, using the indirect enzyme-linked immunosorbent assay (ELISA). Mean absorbance values obtained from the sera ranged from 0.905 with the individual T-58 antigen to 1.760 using an antigen combination (T-58 + T-66 + WI-R). All of the 6 antigenic preparations were able to detect antibody in the serum specimens, but the antigen combinations detected antibody to a higher degree than the individual antigen. This study provides evidence that combinations of the yeast lysate reagents seem to be more efficacious for antibody detection in dog sera, but our laboratory is continuing to evaluate antigen lysate combinations for detection of antibodies in blastomycosis.