Biology subjects

Stafford, P.

Publications and source records attributed to Stafford, P..

3 recordsLinked to original sources

bioRxiv2024

Freezing Diluted Bovine Serum Albumin Standards Does Not Significantly Affect Standard Curves

Total protein isolation followed by quantitation is a common protocol in many laboratories. Quantitation is often done using a colorimetric assay such as the bicinchoninic acid (BCA) assay in which a change in the color of the BCA reagent is related to protein concentration. Extracted protein samples are compared to a standard curve made with dilutions of a protein standard such as bovine serum albumin (BSA) to determine their concentrations. A series of experiments was designed to determine the most reproducible and accurate method for quantifying protein concentrations of samples in an experimental series over time. The effect of freezing on diluted standards was investigated. Standards were frozen at -20{degrees}C or -80{degrees}C and serially thawed and refrozen up to three times prior to their use in a BCA assay. Thawing and refreezing the standards had no significant effect on protein concentration and the resulting standard curves. Inter-person and intra-person variability in the preparation of standards was also investigated. Protein concentration differences due to inter-person and intra-person variability were greater than protein concentration variability resulting from freezing and thawing, regardless of the freezing temperature. The most reproducible and accurate method for determining the protein concentration of extracted samples in an experimental series over time is diluting a large batch of BSA standards and freezing them at either -20{degrees}C or -80{degrees}C. Reproducibility was maintained with up to three freeze-thaws. HighlightsO_LIFreezing diluted BSA standards at either -20{degrees}C or -80{degrees}C and thawing and refreezing them up to three times does not significantly alter their protein concentrations. C_LIO_LIInter-person variability in standard curves is greater than intra-person variability, and controlling for developer decreases variability in most cases. C_LIO_LIThere is more consistency in standard curves when a single batch of diluted standards is aliquoted and frozen at either -20{degrees}C or -80{degrees}C and thawed up to three times than when the same investigator or different investigators make fresh standards before each assay. C_LI

cell biology↗

bioRxiv2022

Modeling the Sequence Dependence of Differential Antibody Binding in the Immune Response to Infectious Disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a wide variety of infectious disease agents. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. Previously, this immune profiling approach or "immunosignature" has been implemented using a purely statistical evaluation of pattern binding, with no regard for information contained in the amino acid sequences themselves. Here, a neural network is trained on immunoglobulin G binding to 122,926 amino acid sequences selected quasi-randomly to represent a sparse sample of the entire combinatorial binding space in a peptide array using human serum samples from uninfected controls and 5 different infectious disease cohorts infected by either dengue virus, West Nile virus, hepatitis C virus, hepatitis B virus or Trypanosoma cruzi. This results in a sequence-binding relationship for each sample that contains the differential disease information. Processing array data using the neural network effectively aggregates the sequence-binding information, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared to the raw binding data. Because the neural network model is trained on all samples simultaneously, the information common to all samples resides in the hidden layers of the model and the differential information between samples resides in the output layer of the model, one column of a few hundred values per sample. These column vectors themselves can be used to represent each sample for classification or unsupervised clustering applications such as human disease surveillance. Author SummaryPrevious work from Stephen Johnstons lab has shown that it is possible to use high density arrays of near-random peptide sequences as a general, disease agnostic approach to diagnosis by analyzing the pattern of antibody binding in serum to the array. The current approach replaces the purely statistical pattern recognition approach with a machine learning-based approach that substantially enhances the diagnostic power of these peptide array-based antibody profiles by incorporating the sequence information from each peptide with the measured antibody binding, in this case with regard to infectious diseases. This makes the array analysis much more robust to noise and provides a means of condensing the disease differentiating information from the array into a compact form that can be readily used for disease classification or population health monitoring.

immunology↗

bioRxiv2022

Dysregulation of stress-induced translational control by Porphyromonas gingivalis in host cells

Periodontitis, a chronic inflammatory gum disease, is caused in part by the periodontopathogen Porphyromonas gingivalis. Infection triggers activation of host inflammatory responses which induce stresses such as oxidative stress. Under such conditions, cells can activate the Integrated Stress Response (ISR), a signalling cascade which functions to determine cellular fate, by either downregulating protein synthesis and initiating a stress-response gene expression program, or if stress cannot be overcome, initiating programmed cell death. Recent studies have implicated the ISR signalling in both host antimicrobial defences and within the pathomechanism of certain microbes. In this study, we investigated how P. gingivalis infection alters translation attenuation during oxidative stress-induced activation of the ISR pathway in oral epithelial cells. P. gingivalis infection alone did not result in ISR activation. In contrast, infection coupled with stress led to differential stress granule formation and composition, along with dysregulation of the microtubule network. Infection also heightened stress-induced translational repression, a response which could not be rescued by ISRIB, a potent ISR inhibitor. Heightened translational repression during stress was observed with both P. gingivalis conditioned media and outer membrane vesicles, implicating the role of a secretory factor, probably proteases known as gingipains, in this exacerbated translational repression. The effects of gingipain inhibitors and gingipains-deficient P. gingivalis mutants further confirmed these pathogen-specific proteases as the effector. Gingipains are known to degrade the mammalian target of rapamycin (mTOR) and these studies implicate the gingipain-mTOR axis as the effector of host translational dysregulation during stress.

microbiology↗