Biology Methods and Protocols最新文献

Drug repurposing presents a promising strategy to accelerate drug discovery by identifying new therapeutic uses for existing compounds, particularly for diseases with limited or no effective treatment options. We introduce DrugPipe, a 'Generative AI-Assisted Virtual Screening Pipeline' developed within the target-centric paradigm of drug repurposing, which aims to discover new indications by identifying compounds that interact with a specific protein target. 'DrugPipe' integrates generative modeling, binding pocket prediction, and similarity-based retrieval from drug databases to enable a scalable and generalizable in silico repurposing workflow. It supports blind virtual screening for any protein target without requiring prior structural or functional annotations, making it especially suited for novel or understudied targets and emerging health threats. By efficiently generating candidate ligands and rapidly retrieving structurally similar approved drugs, 'DrugPipe' accelerates the identification and prioritization of repurposable compounds. In comparative evaluations, it achieves hit rate performance comparable to QVina-W, a widely used blind docking tool, while significantly reducing computational time, highlighting its practical value for large-scale virtual screening and data-scarce repurposing scenarios. The full implementation and evaluation details are available at https://github.com/HySonLab/DrugPipe.

There are approximately 12,000 described species within the class Diplopoda. Only five species, falling within 4 of 16 described orders, have fully sequenced genomes. No whole genomes are available for incredibly diverse families like Xystodesmidae. Furthermore, genetic information attributed to key functions in these species is very limited. There is a growing interest in characterizing genomes of non-model organisms, however, extracting high-quality DNA for organisms with complex morphology can be challenging. Here we describe a detailed methodology for obtaining high-purity DNA from legs, head, and body tissues from wild-caught specimens of the millipede species Cherokia georgiana. Our dissection protocol separates the digestive tract minimizing microbial abundance in the extracted DNA sample. We describe sample homogenization steps that improve total DNA yield. To assess sample quality, concentration, and size we use spectrophotometry, fluorometry, and automated electrophoresis, respectively. We consistently obtain average DNA length upwards of 12-25 kb. We applied Oxford Nanopore Technologies MinION long-read sequencing, an affordable and accessible option with potential for field-based applications. Here we present tissue-specific DNA sequencing metrics, alignment and assembly of mitochondrial DNA consensus sequence, and phylogenetic analysis. While noting the limitations of our nanopore-based sequencing methodology, we provide a framework to process field specimens for PCR-free DNA sequencing data that can be used for gene-specific alignment and analysis.

Protein succinylation is a vital post-translational modification (PTM) that involves the covalent attachment of a succinyl group (-CO-CH2-CH2-CO-) to the lysine residue of a protein molecule. The mechanism underlying the succinylation process plays a critical role in regulating protein structure, stability, and function, contributing to various biological processes, including metabolism, gene expression, and signal transduction. Succinylation has also been associated with numerous diseases, such as cancer, neurodegenerative disorders, and metabolic syndromes. Due to its important roles, the accurate prediction of succinylation sites is essential for a comprehensive understanding of the mechanisms underlying succinylation. Although research on the identification of protein succinylation sites has been increasing, experimental methods remain time-consuming and costly, underscoring the need for efficient computational approaches. In this study, we present KD_MultiSucc, a model for cross-species prediction of succinylation sites using Multi-Teacher Knowledge Distillation and Word Embedding. The proposed method leverages the strengths of both Knowledge Distillation and Word Embedding techniques to reduce computational complexity while maintaining high accuracy in predicting protein succinylation sites across species. Experimental results demonstrate that the proposed predictor outperforms existing predictors, providing a valuable contribution to PTM research and biomedical applications. To assist readers and researchers, the codes and resources related to this work have been made freely accessible on GitHub at https://github.com/nuinvtnu/KD_MultiSucc/.

The skin is the largest organ in the body and is the site for a diverse set of diseases. Yet, given the complexity of the cutaneous tissue, there is a limited availability of data in the literature on skin proteomics. Here, we proposed an adapted and optimized protocol for the extraction of proteins from human skin, using a combination of chemical and mechanical lysis approaches. For this, we used of a lysis buffer containing 2% SDS, 50 mM TEAB, and a 1% protease and phosphatase inhibitor cocktail, in addition to Matrix A beads and a FastPrep-24 5G homogenizer. For the characterization of the samples, the obtained proteins were purified and digested using the SP3 method (Single-pot, solid phase, sample preparation), and analyzed by nano liquid chromatography coupled with tandem mass spectrometry. In this way, we were able to identify around 6000 proteins in the skin samples from healthy individuals and patients with the fungal infection sporotrichosis. Our improved methodology could significantly enrich our understanding of skin biology and provide new perspectives for the discovery of biomarkers and therapeutic targets for cutaneous diseases.

The remarkable capacity of the liver to regenerate its lost mass after resection makes living donor liver transplantation a successful treatment option. However, donor heterogeneity significantly influences recovery trajectories, highlighting the need for individualized monitoring. With the rising incidence of liver diseases, safer transplant procedures and improved donor care are urgently needed. Current clinical markers provide only limited snapshots of recovery, making it challenging to predict long-term outcomes. Following partial hepatectomy, precise liver mass recovery requires tightly regulated hepatocyte proliferation. We identified distinct gene expression patterns associated with liver regeneration by analyzing blood-derived gene expression measurements from twelve donors followed over a year. Using a deep learning-based framework, we integrated these patterns with a mathematical model of hepatocyte transitions to develop a personalized, progressive mechanistic digital twin-a virtual liver model that predicts donor-specific recovery trajectories. Central to our approach is a mechanistically identifiable latent space, defined by variables derived from a physiologically grounded differential equation model of liver regeneration, which enables biologically interpretable, bidirectional mapping between gene expression data and model dynamics. This approach integrates clinical genomics and computational modeling to enhance post-surgical care, ensuring safer transplants and improved donor recovery.

Efficient interrogation of neurobiology remains bottlenecked by obtaining mature neurons. Immortalized cell lines still require lengthy differentiation periods to obtain neuron-like cells, which may not efficiently differentiate and are challenging to transfect with plasmids relative to other cell lines such as HEK-293's. To overcome challenges with limited access to cells that express mature neuronal proteins, we knocked out the RE1-silencing transcription factor (REST) from HEK-293's to create a novel neuron-like cell, which we name Neuro293. RNA-sequencing and bioinformatics analyses revealed a significant upregulation of genes associated with neurobiology and membrane excitability including pre-/post-synaptic proteins, voltage gated ion channels, neuron-cytoskeleton, as well as neurotransmitter synthesis, packaging, and release. Western blot validated the upregulation of Synapsin-1 (Syn1) and Snap-25 as two neuron-restricted proteins, as well as the potassium channel Kv1.2. Immunocytochemistry against Neurofilament 200 kd revealed a significant upregulation and accumulation in singular processes extending from Neuro293's cell body. Similarly, while Syn1 increased in the cell body, Syn1 protein accumulated at the ends of processes extruding from Neuro293's. Neuro293's express reporter-genes through the Syn1 promoter after infection with adeno-associated viruses (AAV). However, transient transfection with AAV2 plasmids led to leaky expression through promoter-independent mechanisms. Despite an upregulation of many voltage-gated ion channels, Neuro293's do not possess excitable membranes. Collectively, REST-knockout in HEK-293's induces a quickly dividing and easily transfectable cell line that expresses neuron-restricted and mature neuronal proteins which can be used for high-throughput biochemical interrogation, however, without further modifications neither HEK-293's or Neuro293's exhibit properties of excitable membranes.

This study introduces an approach to classifying histopathological images for detecting dysplasia in oral cancer through the fusion of support vector machine (SVM) classifiers trained on deep learning features extracted from InceptionResNet-v2 and vision transformer (ViT) models. The classification of dysplasia, a critical indicator of oral cancer progression, is often complicated by class imbalance, with a higher prevalence of dysplastic lesions compared to non-dysplastic cases. This research addresses this challenge by leveraging the complementary strengths of the two models. The InceptionResNet-v2 model, paired with an SVM classifier, excels in identifying the presence of dysplasia, capturing fine-grained morphological features indicative of the condition. In contrast, the ViT-based SVM demonstrates superior performance in detecting the absence of dysplasia, effectively capturing global contextual information from the images. A fusion strategy was employed to combine these classifiers through class selection: the majority class (presence of dysplasia) was predicted using the InceptionResNet-v2-SVM, while the minority class (absence of dysplasia) was predicted using the ViT-SVM. The fusion approach significantly outperformed individual models and other state-of-the-art methods, achieving superior balanced accuracy, sensitivity, precision, and area under the curve. This demonstrates its ability to handle class imbalance effectively while maintaining high diagnostic accuracy. The results highlight the potential of integrating deep learning feature extraction with SVM classifiers to improve classification performance in complex medical imaging tasks. This study underscores the value of combining complementary classification strategies to address the challenges of class imbalance and improve diagnostic workflows.

Cancer treatments often lose effectiveness as tumors develop resistance to single-agent therapies. Combination treatments can overcome this limitation, but the overwhelming combinatorial space of drug-dose interactions makes exhaustive experimental testing impractical. Data-driven methods, such as machine and deep learning, have emerged as promising tools to predict synergistic drug combinations. In this work, we systematically investigate the use of categorical embeddings within Deep Neural Networks to enhance drug synergy predictions. These learned and transferable encodings capture similarities between the elements of each category, demonstrating particular utility in scarce data scenarios.

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.

Thoracic aortic aneurysm and dissection (TAD) is a life-threatening vascular disorder, and smooth muscle cell mitochondrial dysfunction leads to cell death, contributing to TAD. Accurate measurements of metabolic processes are essential for understanding cellular homeostasis in both healthy and diseased states. While assays for evaluating mitochondrial respiration have been well established for cultured cells and isolated mitochondria, no optimized application has been developed for aortic tissue. In this study, we generate an optimized protocol using the Agilent Seahorse XFe24 analyzer to measure mitochondrial respiration in mouse aortic tissue. This method allows for precise measurement of mitochondrial oxygen consumption in mouse aorta, providing a reliable assay for bioenergetic analysis of aortic tissue. The protocol offers a reproducible approach for assessing mitochondrial function in aortic tissues, capturing both baseline OCR and responses to mitochondrial inhibitors, such as oligomycin, FCCP, and rotenone/antimycin A. This method establishes a critical foundation for studying metabolic shifts in aortic tissues and offers valuable insights into the cellular mechanisms of aortic diseases, contributing to a better understanding of TAD progression.