Biology Methods and Protocols最新文献

The discovery of antibodies through phage display is significantly influenced by antigen presentation during panning, particularly for membrane-anchored proteins, which pose challenges due to their complex structures. Traditional approaches, such as whole cells expressing the target protein, often result in low antigen density and high background signals. In this study, we describe an alternative method using stably transfected cell lines that express the target antigen on their surface, regulated by an intracellular enhanced green fluorescent protein (EGFP) signal. This system enables high-throughput flow cytometry-based screening of phage display libraries to isolate human antibodies that recognize the native conformation of membrane proteins. Using human epithelial cell adhesion molecule (EpCAM) and human neuroplastin 65 (NP65) as model antigens, we established an optimized screening workflow with polyclonal phage pools. Selected EpCAM-specific single-chain variable fragments (scFvs) from a naïve library were recombinantly expressed with an IgG4 scaffold and characterized for specific binding. This approach provides an effective platform for the identification of antibodies against membrane proteins in their native state.

[This corrects the article DOI: 10.1093/biomethods/bpae098.].

Protein homeostasis (proteostasis) is the balance of protein synthesis, protein maintenance, and degradation. Loss of proteostasis contributes to the aging process and characterizes neurodegenerative diseases. It is well established that the processes of protein maintenance and degradation are declining with aging; however, the contribution of a declining quality of protein synthesis to the loss of proteostasis is less well understood. In fact, protein synthesis at the ribosome is an error-prone process and challenges the cell with misfolded proteins. Here, we present the development of a highly sensitive and reproducible reporter assay for the detection of translational errors and the measurement of translational fidelity. Using Nano-luciferase, an enzyme 3 times smaller and 50 times more sensitive than the hitherto used Firefly-luciferase, we introduced stop-codon and amino-acid exchanges that inactivate the enzyme. Erroneous re-activation of luciferase activity indicates ribosomal inaccuracy and translational infidelity. This highly sensitive and reproducible method has broad applications for studying the molecular mechanisms underlying diseases associated with defective protein synthesis and can be used for drug screening to modulate translational fidelity.

People infected with coronavirus disease-19 (COVID-19) may continue to experience symptoms for several weeks or even months after acute infection, a condition known as long COVID. Cognitive problems such as memory loss are among the most commonly reported symptoms of long COVID. However, a comprehensive evaluation of the risks of cognitive decline following COVID-19 infection among different sociodemographic groups has not been undertaken at the national level in the USA. We conducted a secondary analysis on the datasets from the U.S. Census Bureau Household Pulse Survey, encompassing data collected from 1 June 2022 to 19 December 2022. Based on a cohort of 385 370 individuals aged 18 years or older, we employed logistic regression analysis to examine the association between self-reported cognitive deficits and different sociodemographic factors among individuals with long COVID conditions. We have demonstrated that individuals with long COVID had a significantly higher risk of cognitive deficits compared to those with no history of COVID infection. Cognitive deficits vary across sociodemographic groups. In individuals without long COVID, men, older adults, and those with higher education reported fewer cognitive deficits, while Hispanics and residents of the South reported more. Long COVID had similar impacts across genders and regions but appeared to have the smallest impact on Hispanics compared to other racial groups. Conversely, the effects of long COVID were most significant in older adults and individuals with higher education. The state-level analysis further suggests potential variation in long COVID's effects across different states. The risks of cognitive deficits among adults with post-COVID conditions are substantial. Various sociodemographic groups can have different risks of developing cognitive deficits after experiencing long COVID. The findings of this large-scale study can help identify sociodemographic groups at higher risk of cognitive deficits, facilitate medical interventions, and guide resource allocation to target populations at risk and prioritize areas with a high rate of cognitive decline.

Nuclear isolation is crucial for studying gene expression and regulatory mechanisms in eukaryotic cells. This study aimed to improve nuclear isolation and compare the yield, purity, and efficiency of several methods. Human umbilical vein endothelial cells were used to evaluate four different techniques: sucrose centrifugation, a simplified method, homogenization, and the NE-PER kit. For sucrose centrifugation, cells were scraped in Tween buffer, washed with sucrose buffer, and homogenized in a Dounce homogenizer. The pellet was washed with glycerol buffer to isolate the nuclei. In the simplified method, cells were scraped in scraping buffer, washed with sucrose buffer, and the pellet was washed with glycerol buffer to isolate the nuclei. For homogenization, cells were washed with phosphate buffered saline, followed by two washes in extract buffer and lysed with 10 strokes in a Kontes Dounce homogenizer. The NE-PER kit was used according to the manufacturer's protocol. Nuclei isolated by each method were tested by immunoblotting, co-immunoprecipitation (Co-IP), and chromatin immunoprecipitation (Ch-IP) assays. The simplified method produced nuclei with fewer organelles and less cytoplasm than those isolated by homogenization or the NE-PER kit. It was similarly effective as sucrose centrifugation but faster. Co-IP and Ch-IP assays confirmed that the simplified method enriched target proteins and DNA fragments. Overall, the simplified method provides a highly pure nuclear sample optimal for downstream applications requiring purified nuclei.

Fungi are eukaryotic organisms grouped based on different traits of their morphology. In 1970, R. W. Rayner published A Mycological Colour Chart to provide a standardized system for identifying color in fungi. While its terminologies have contributed a standard way of color matching for taxonomic diagnoses, this method using the personal color perception of the observer does not guarantee accuracy. Considering the diversity of fungi, visual color matching is expected to be challenging without a standard assisting instrument. In this study, the R package PUPMCR is developed to approximate the color name and associated pigments of fungal species based on the pixel coordinates of its uploaded image. This software utilizes CIELAB and RGB color spaces as well as Euclidean and Chi-square distance metric systems. The package is tested and validated using 300 fungal images as a dataset for conducting interrater reliability tests. Results showed the highest agreement for parameters utilizing the RGB color space (Cohen's kappa values: 0.655 ± 0.013 for RGB and Euclidean; 0.658 ± 0.004 for RGB and Chi-square), attributed to its computational efficiency, which facilitates more uniform binning and universally scaled distance metrics. The produced color-identifying tool is also available as a Shiny web application (https://pupmcr.shinyapps.io/PUPMCR/) to allow better accessibility for users on the World Wide Web. The development of PUPMCR not only benefits a variety of users from its free accessibility but also provides a more reliable color identification system in the field of mycology.

A longstanding challenge in biology is accurately analyzing images acquired using microscopy. Recently, machine learning (ML) approaches have facilitated detailed quantification of images that were refractile to traditional computation methods. Here, we detail a method for measuring pigments in the complex-mosaic adult Drosophila eye using high-resolution photographs and the pixel classifier ilastik [1]. We compare our results to analyses focused on pigment biochemistry and subjective interpretation, demonstrating general overlap, while highlighting the inverse relationship between accuracy and high-throughput capability of each approach. Notably, no coding experience is necessary for image analysis and pigment quantification. When considering time, resolution, and accuracy, our view is that ML-based image analysis is the preferred method.

Alzheimer's disease (AD) is a multifactorial systemic disease that is triggered, at least in part, by the accumulation of β-amyloid (Aβ) peptides in the brain, but it also depends on immune system-mediated regulation. Recent studies suggest that B cells may play a role in AD development and point to the accumulation of clonally expanded B cells in AD patients. However, the specificity of the clonally expanded B cells is unknown, and the contribution of Aβ-specific B cells to AD pathology development is unclear. In this study, we have developed a novel method to identify Aβ-specific B cells by flow cytometry using fluorescent tetramers. The suggested method also enables the identification of B-cell clones specific to a more pathology-provoking form of Aβ with an isomerized Asp7 residue (Iso-D7-Aβ) that accumulates in elderly people and in AD patients. The method has been verified using mice immunized with antigens containing the isomerized or non-isomerized Aβ N-terminus peptides. In addition, we describe a new method for the detection of Iso-D7-Aβ-specific antibodies, which was tested on mouse serum. These methods are of potential importance in research aimed at studying AD and may be also utilized for diagnostic and therapeutic purposes.

A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference. Specifically, motivated by the clustered distribution of known RNA structures, a collection of distinct DL models is trained by iteratively leaving one cluster out. Each DL model hence serves as an expert on all but one cluster in the training data. Consequently, for an ID sequence, all but one DL model makes accurate predictions consistent with one another, while an OOD sequence yields highly inconsistent predictions among all DL models. Through consensus analysis of DL predictions, test sequences are categorized as ID or OOD. ID sequences are subsequently predicted by averaging the DL models in consensus, and OOD sequences are predicted using physics-based models. Instead of remediating generalization gaps with alternative approaches such as transfer learning and sequence alignment, MoEFold2D circumvents unpredictable ID-OOD gaps and combines the strengths of DL and physics-based models to achieve accurate ID and robust OOD predictions.

Real time-polymerase chain reaction (RT-PCR) is used routinely in clinical practice as a cost-effective method for molecular diagnostics. Research in pediatric B-cell Acute Lymphoblastic Leukemia (ped B-ALL) suggests that apart from cytogenetics and clinical features, there is a need to include Copy number variation (CNV) in select genes at diagnosis, for upfront stratification of patients. Using ped B-ALL as a model, we have developed a RT-PCR-based iterative probability scoring method for reporting CNVs, and relative gene-expression changes. Our work highlights that once genes of interest and hotspots of CNVs are identified in discovery phase, our proposed method can be used as a cost-effective and user-friendly diagnostic tool for the identification of changes at genomic or transcriptomic level. It has the potential to be incorporated in routine diagnostics in resource constrained settings and be tailored for different diseases as per need.