Biology Methods and Protocols最新文献

The CATH database is a free publicly available online resource that provides annotations about the evolutionary and structural relationships of protein domains. Due to the flux of protein structures coming mainly from the recent breakthrough of AlphaFold and therefore the non-feasibility of manual intervention, the CATH team recently developed an automatic CATH superfamily (SF) classifier called CATHe, which uses a feed-forward neural network (FNN) classifier with protein Language Model (pLM) embeddings as input. Using the same dataset of remote homologues (with a 20% sequence identity threshold), this paper presents CATHe2, which improves on CATHe by switching the old pLM ProtT5 for one of the most recent versions called ProstT5, and by incorporating domain 3D information into the classifier through Structural Alphabet representation, specifically, 3Di sequence embeddings. Finally, CATHe2 implements a new version of the FNN classifier architecture, fine-tuned to perform at the CATH superfamily prediction task. The best CATHe2 model reaches an accuracy of 92.2% ± 0.7% with an F1 score of 82.3% ± 1.3%, which constitutes an improvement of 9.9% on the F1 score and 6.6% on the accuracy, from the previous CATHe version (85.6% ± 0.4% accuracy and 72.4% ± 0.7% F1 score) on its largest dataset (∼1700 superfamilies). This model uses ProstT5 amino acid (AA) sequence and 3Di sequence embeddings as input to the classifier, but a simplified version requiring only AA sequences, already improves CATHe's F1 score by 6.7% ± 1.3% and accuracy by 6.6% ± 0.7% on its largest dataset.

Accurate and timely diagnosis of colorectal cancer (CRC) is essential for effective treatment and better patient outcomes. This study explores the application of deep learning (DL) for automated CRC categories classification using hematoxylin and eosin-stained histopathology (H&E) images. Among the models, ResNet-34 demonstrated a strong balance of performance and complexity, achieving an overall accuracy of 85.04%, with top-2 and top-3 classification accuracies of 96.68% and 99.23%, respectively. ResNet-50 exhibited the highest micro-averaged ROC AUC of 0.9933 and F1-score of 87.51%. Swin Transformer V2 model also showed competitive results, with Swin v2-t-w8 achieving particularly high accuracy in Hyperplasia polyp detection (95.83%) and Adenocarcinoma (93.33%), alongside strong ROC AUCs (0.9926 for Hyperplasia polyp and 0.9864 for Adenocarcinoma), though at the cost of increased computational demands. We further developed a two-stage prediction framework comprising a binary abnormal detection stage followed by a multiclass cancer classifier. This approach substantially improved classification robustness, particularly for underrepresented and morphologically complex classes. Particularly, High-grade dysplasia classification accuracy improved from 53.57% with ResNet-34 to 71.43% in its two-stage extension. These results suggest that moderate-depth architectures can effectively capture the morphological diversity of colorectal cancer stages and provide an interpretable, efficient deep learning-based diagnostic tool to support pathologists.

Conventional methods for measuring glutathione peroxidase (GPx) activity are limited by interference issues, complex protein precipitation steps, and variable reliability, necessitating the development of improved analytical approaches for both research and clinical applications. A modified GPx activity assay has been developed utilizing the Tiron reagent system, which eliminates the need for protein precipitation. The protocol employs a novel termination reagent containing ferrous ion (Fe²⁺) and Tiron (C₆H₄Na₂O₈S₂) to instantly stop enzymatic decomposition of hydrogen peroxide. Following GPx-mediated H₂O₂ consumption, residual hydrogen peroxide undergoes Fenton-type redox reactions with Fe²⁺ ions, generating Fe³⁺ species that coordinate with Tiron through catechol moieties to form a stable ferri-Tiron complex [Fe(C₆H₄Na₂O₈S₂)]³⁺. The assay operates optimally at acidic pH to ensure complex stability and minimize interference from competing reactions. The modified protocol demonstrates superior performance characteristics compared to conventional GPx assays, including elimination of interference effects, enhanced accuracy and precision, and improved reproducibility across diverse sample matrices. The method's spectrophotometric detection system provides reliable quantification with minimal matrix effects, while the simplified workflow reduces technical complexity and analysis time. This interference-free GPx activity assay offers significant advantages for both laboratory research and clinical diagnostics. It achieves this through a combination of analytical precision, operational simplicity, and broad compatibility with standard laboratory practices and equipment. The protocol's robust performance at acidic pH conditions, coupled with its elimination of protein precipitation steps, establishes it as a valuable alternative to existing methodologies for assessing oxidative stress and evaluating antioxidant capacity.

Multiplex PCR-based assays are indispensable platforms for rapid and cost-effective DNA-based multi-target detection. The success of such an assay highly depends on the accurate design of oligonucleotide primers, arguably its most vital component. In this study, the ThermoPlex design tool is introduced, offering an automated design pipeline for target-specific multiplex PCR primers motivated by DNA thermodynamics. From a sequence alignment of all relevant target and non-target sequences, ThermoPlex automatically designs multiplex PCR primer candidates in just a matter of minutes. The software also offers tools for thermodynamic calculations that can either be used apart from the automated primer screening routine or in conjunction with other existing primer design tools, depending on the needs of the user. Evidence presented in this study provides insights into the performance of the software performance through theoretical and experimental analyses, serving to establish the reliability of its framework.

The long noncoding RNA NEAT1 is transcribed from a single exon gene and produces two isoforms through alternative 3'-end processing. The short polyadenylated NEAT1_1 drives proliferation in many malignancies through increasing glycolytic flux and the Warburg effect. The longer NEAT1_2 lacks a poly(A)-tail but is an essential scaffold for nuclear paraspeckles, nuclear condensates that reportedly play a tumour protective role. Due to the two isoforms sharing identical 5'-ends, many previous studies have quantified NEAT1_1 by subtracting NEAT1_2 from total NEAT1 levels. However, this only estimates the abundance of NEAT1_1. Standard oligo(dT)-primed RT-PCR is not suitable for quantifying NEAT1_1 as the longer NEAT1_2 sequence contains twelve poly(A) repeats, so unintended priming overestimates NEAT1_1 abundance. Here, we report the development of a novel RT-PCR method allowing relative quantification of NEAT1_1 independently of NEAT1_2. Using an anchored oligo(dT) primer for reverse transcription enriches cDNA with the NEAT1_1 isoform, and the use of a longer primer anchoring sequence at the PCR stage enhances detection specificity. Our method is validated by the successful independent quantification of NEAT1_1 following a forced isoform switch using antisense oligomers in both cancer and non-cancer cell lines. Additionally, we have visualized this isoform switch in colorectal cancer cell lines using fluorescent in situ hybridization techniques specific to NEAT1_2-containing paraspeckles.

Accurate and non-invasive measurement of cell membrane potential is essential for studying physiological processes and disease mechanisms. In this study, we propose a conceptual and computational model for estimating membrane potential based on the electrical behavior of two series-connected capacitors, simulating a cell-attached patch-clamp configuration. We hypothesize that the presence of a net intracellular charge-representing the membrane potential-affects the charging and discharging characteristics of the capacitive circuit by introducing asymmetry in voltage distribution. To test this, we used LTSpice to simulate 202 different capacitor configurations, varying the internal potential from -100 mV to -10 mV in 10 mV increments. For each configuration, we applied voltage pulses and extracted four key current features: maximum and minimum amplitudes, and total charge and discharge durations. These features were used to train a machine learning model (XGBRegressor), which, despite the limited dataset size, demonstrated strong predictive performance (R ² = 0.90, RMSE = 13.79 mV) in estimating the internal potential. Our findings support the hypothesis that membrane potential can be inferred from capacitive current responses in a non-invasive, cell-attached configuration. This simulation-based framework offers a promising foundation for the non-invasive estimation of membrane potential and warrants further validation in experimental systems.

Measurement of the oxygen consumption rate, or respirometry, is a powerful and comprehensive method for assessing mitochondrial function both in vitro and in vivo. Respirometry at the whole-organism level has been repeatedly performed in the model organism Caenorhabditis elegans, typically using high-throughput microplate-based systems over traditional Clark-type respirometers. However, these systems are highly specialized, costly to purchase and operate, and inaccessible to many researchers. Here, we develop a respirometry assay using low-cost commercially available optical oxygen sensors (PreSens OxoPlates^®) and fluorescence plate readers (the BMG FLUOstar), as an alternative to more costly standard respirometry systems. This assay uses standard BMG FLUOstar protocols and a set of custom scripts to perform repeated measurements of the C. elegans oxygen consumption rate, with the optional use of respiratory inhibitors or other interventions. We validate this assay by demonstrating the linearity of basal oxygen consumption rates in samples with variable numbers of animals, and by examining the impact of respiratory inhibitors with previously demonstrated efficacy in C. elegans: carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (a mitochondrial uncoupler) and sodium azide (a Complex IV inhibitor). Using this assay, we demonstrate that the sequential use of FCCP and sodium azide leads to an increase in the sodium azide-treated (non-mitochondrial) oxygen consumption rate, indicating that the sequential use of respiratory inhibitors, as standard in intact cell respirometry, may produce erroneous estimates of non-mitochondrial respiration in C. elegans and thus should be avoided.

DNA methylation is an important modification in the genomes, participating in gene expression or gene repression, as a part of epigenetic studies. This modification can be studied with last-generation sequencing or using PCR coupled with High Resolution Melting (HRM). For this, primers used need to be correctly designed, since the use of specific DNA standards is required, which have specific temperatures displayed in the analyses. We propose and show a method for HRM methylation analysis based on targeted-sequences nucleotide proportion, developed in the Health Laboratory in El Colegio de la Frontera Sur (ECOSUR), Chiapas. We found that when DNA nucleotides in the predicted amplicon have a certain proportion (A-T and G-C), melting curves in the HRM analyses behave differently. Besides, other modifications can be made to primers, such as the number of CpG motifs included within the sequence. DNA nucleotide proportion is shown to be an easy but reliable way of doing primer design when other methods are not available, either because of the lack of resources or the unavailability of sequencing equipment. Additionally, this methodological approach could help reduce time and reagent waste during standardization by improving primer selection efficiency in multi-gene studies.

Bovine serum albumin (BSA) nanoparticles have attracted a lot of interest as biocompatible and biodegradable carriers for a range of pharmacological and biological uses. BSA nanoparticles have several advantages over other types of nanoparticles, including their ability to increase the stability and solubility of encapsulated drugs, their non-toxicity, and their ease of surface modification. Cancer treatment, immunological modulation, enzyme immobilization, controlled release systems, bioimaging, and theranostics are some of its potential applications. This protocol offers a detailed and accessible methodology for the synthesis, drug encapsulation, and characterization of albumin nanoparticles, with particular emphasis on reproducibility and adaptability. The synthesis uses the desolvation process and crosslinking with the compound glutaraldehyde for stability. The crosslinking ratio, pH, and BSA content are important factors that can be adjusted to control size, surface charge, and dispersity. The methods used for characterization are described in detail, including dynamic light scattering for particle size and zeta potential, transmission and scanning electron microscopy for morphology, Fourier-transform infrared spectroscopy, and nanoparticle tracking analysis for stability assessment. The stability of the nanoparticles was evaluated under physiologically relevant ionic and pH conditions by dispersing them in phosphate-buffered saline, providing insight into their colloidal behavior in a simulated physiological environment. This technique facilitates the design of functionalized BSA nanoparticles for certain biomedical and therapeutic applications by acting as a fundamental reference for researchers. This work promotes innovation in nanoparticle-based technology and advances the field by standardizing preparation and characterization techniques.

Cancer remains one of the most complex diseases faced by humanity, with over 200 distinct types, each characterized by unique molecular profiles that demand specialized therapeutic approaches. [Tomczak et al. (Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Współczesna Onkol 2015;1A:68-77.)] Prior studies have shown that both short and long telomere lengths (TLs) are associated with elevated cancer risk, underscoring the intricate relationship between TL variation and tumorigenesis. [Haycock et al. (Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study. JAMA Oncol 2017;3:636-51.)] To investigate this relationship, we developed a supervised machine learning model trained on telomeric read content, genomic variants, and phenotypic features to predict tumor status. Using data from 33 cancer types within The Cancer Genome Atlas (TCGA) program, our model achieved an accuracy of 82.62% in predicting tumor status. The trained model is available for public use and further development through the project's GitHub repository: https://github.com/paribytes/TeloQuest. This work represents a novel, multidisciplinary approach to improving cancer diagnostics and risk assessment by integrating telomere biology with Biobank-scale genomic and phenotypic data. Furthermore, we highlight the potential of TL variation as a meaningful predictive biomarker in oncology.