Biology Methods and Protocols最新文献

Obesity is defined as a disease in which abnormal excessive body fat accumulation causes adverse effects on health. One proposed contributing factor to the rise in obesity is the exposure to endocrine disruptors acting as obesogens. Semitransparent zebrafish larvae, with their well-developed white adipose tissue (WAT), offer a unique opportunity for studying the effects of toxicant chemicals and pharmaceuticals on adipocyte dynamics and whole-organism adiposity in a vertebrate model. The work presented here is a detailed optimized zebrafish obesogenic test (ZOT) protocol. The method allows to assess the effects of diet composition, drugs and environmental contaminants, acting as obesogens or anti-obesogens, alone or in combination, on WAT levels in zebrafish larvae. Zootechnical parameter guidelines, including larvae rearing conditions, feeding, and selection of larvae to be enrolled are provided. An optimized procedure for in vivo staining of adipocyte lipid droplets with Nile Red before and after exposure to compounds is provided to enhance reproducibility. Using suitable subcutaneous WAT locations, a rationally defined guide for wide-field fluorescence microscopy signal acquisition is proposed. The ZOT analysis software was developed to enable automated and efficient image data processing by using custom-trained supervised deep-learning models. The present ZOT protocol distinguishes intrinsic variability of the test method from the biological effect measured. It is the basis of a specific, sensitive, and robust quantitative in vivo assay for high-throughput screening of compounds and food content that influence adipocyte hyper/hypotrophy. As such, it provides relevant information for environmental as well as human risk and benefit assessments.

γ-aminobutyrate (GABA) is a non-proteinogenic amino acid produced by glutamate decarboxylase (GAD) that functions as a vital neurotransmitter in animals, and as an important metabolite and signaling molecule in plants and microbes. "GABase" consists of a mixture of recombinant GABA transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSDH) that is widely used for spectrophotometric quantification of glutamate decarboxylase (GAD) activity or GABA levels in tissue extracts. Both can be conveniently monitored at 340 nm owing to the sequential conversion of GABA into succinate by GABA-T and SSDH, and concomitant reduction of NADP⁺ into NADPH by SSDH. Currently, these assays rely on commercially available GABase from Pseudomonas fluorescens. However, the excessive cost of commercial GABase prompted us to develop an inexpensive and rapid "DIY" method for producing GABase by cloning, expressing and purifying His₆-tagged GABA-T and SSDH from Escherichia coli. We validated our in-house GABase preparation by comparing GAD activities and GABA levels of the model plant Arabidopsis thaliana with those obtained using commercial GABase. Both pET30a plasmids for expressing E. coli His₆-GABA-T and His₆-SSDH have been deposited into AddGene (www.addgene.com). Our protocols for producing and using recombinant E. coli GABase should be of interest to any researcher who studies eukaryotic or prokaryotic GABA and/or GAD activity.

The precise determination of viral titers in virological studies is a critical step to assess the infectious viral concentration of a sample. Although conventional titration methods, such as endpoint dilution or plaque forming units are the gold standards, their widespread use for screening experiments remains limited due to the time-consuming aspect and resource-intensive requirements. This study introduces a rapid and user-friendly high-throughput screening assay for evaluating viral titers. The colorimetric method used relies upon assessing virus-induced cytopathic effects by measuring the reduction of a tetrazolium reagent to formazan through cellular dehydrogenation within mitochondria. The resulting formazan quantity is correlated with the viral titer and can be easily quantified by a colorimetric measurement. In this perspective, this manuscript describes two case studies for the titration of the porcine respiratory coronavirus virus and bovine alpha herpesvirus 1, highlighting, respectively, a linear regime between 100 and 2000 TCID₅₀/ml and 500- ${10}^6$ PFU/ml for rapid titration within these ranges. The proposed technique's advantages and drawbacks are discussed, along with potential applications such as drug screening and the assessment of viral survival on inert surfaces.

Cells respond to environmental stresses such as cold and osmotic stresses. These stresses induce signal transduction pathways in cells. However, the molecular mechanisms activated by cold and osmotic stresses in higher eukaryotes remain elusive. Previously, we described a reporter system utilizing inducible translocation trap that detects nuclear translocation of 2-amino-3-ketobutyrate coenzyme A ligase (KBL) in response to cold and osmotic stresses. In the present study, we developed additional reporter systems to detect intracellular events induced by these stresses. These reporter systems will be instrumental to elucidate the intracellular signaling mechanisms activated by these stresses.

Leptospirosis poses substantial challenges to global public health. In Malaysia, leptospirosis is endemic, with annual cases peaking during the monsoon season. The microscopic agglutination test (MAT) is the gold-standard serological method for confirmation of leptospirosis. However, it is labor-intensive and time-consuming, as it relies on the subjective interpretation of medical lab technicians. This study describes the development of a semiautomated workflow for Leptospira screening by integrating a TensorFlow and custom-designed Keras-based Deep Convolutional Neural Network (DCNN) with conventional MAT. We used a dataset of 442 positive and 442 negative MAT images, which consisted of a mixture of Leptospira serovars from Malaysia to train the model. The model was subjected to hyperparameter tuning, which modulated the number of convolutional layers, filters, kernel sizes, units in dense layers, activation functions, and learning rate. Verification of our tested model compared to the verified patient MAT results achieved the following metrics: a Precision score of 0.8125, a Recall of 0.9286, and an F1-Score of 0.8667. Combining our model with the current Malaysia Leptospira workflow can significantly speed up, reduce inaccuracies, and improve the management of leptospirosis. Furthermore, the application of this model is practical and adaptable, making it suitable for other labs that observe MAT as their Leptospira diagnosis. To our knowledge, this approach is Malaysia's first hybrid diagnostic approach for Leptospira diagnosis. Scaling up the dataset would enhance the model's accuracy, making it adaptable in other regions where leptospirosis is endemic.

Point-of-care (POC) diagnostics have revolutionized disease detection by enabling rapid, on-site testing without the need for centralized laboratory infrastructure. This review presents recent advances in aptamer-functionalized nanoparticles (AFNs) as promising tools for enhancing POC diagnostics, particularly in infectious diseases and cancer. Aptamers, with their high specificity, stability, and modifiability, offer significant advantages over antibodies, while nanoparticles contribute multifunctionality, including signal amplification and targeting capabilities. AFNs have demonstrated up to a 2-10 times increase in detection sensitivity and significant reductions in diagnostic timeframes. We discuss various nanoparticle types, conjugation strategies, real-world applications, and highlight innovative developments such as AI-assisted aptamer design, wearable diagnostic devices, and green nanoparticle synthesis. Challenges related to stability, manufacturing scalability, regulatory hurdles, and clinical translation are critically examined. By merging aptamer precision with nanoparticle versatility, AFNs hold transformative potential to deliver rapid, affordable, and decentralized healthcare solutions, especially in resource-limited settings. Future interdisciplinary research and sustainable practices will be pivotal in translating AFN-based diagnostics from promising prototypes to global healthcare standards.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of accurate models to predict disease dynamics and guide public health interventions. This study leverages the COVASIM agent-based model to simulate 1331 scenarios of COVID-19 transmission across various social settings, focusing on the school, community, and work contact layers. We extracted complex network measures from these simulations and applied deep learning algorithms to predict key epidemiological outcomes, such as infected, severe, and critical cases. Our approach achieved an $R^2$ value exceeding 95%, demonstrating the model's robust predictive capability. Additionally, we identified optimal intervention strategies using spline interpolation, revealing the critical roles of community and workplace interventions in minimizing the pandemic's impact. The findings underscore the value of integrating network analytics with deep learning to streamline epidemic modeling, reduce computational costs, and enhance public health decision-making. This research offers a novel framework for effectively managing infectious disease outbreaks through targeted, data-driven interventions.

Proteins are essential for life in all organisms: they mediate cell signaling and cell division and provide structure/motility to cells and tissues. All proteins are synthesized on cytoplasmic ribosomes as unfolded precursors that need to find their correct location in the compartmentalized cell. In eukaryotes, ∼30% of the proteome is translocated across or integrated into the endoplasmic reticulum (ER) membrane, a process mostly mediated by the heterotrimeric Sec61 complex that spans the ER membrane. There is significant interest in identifying small-molecule inhibitors of the Sec61 translocon channel that hold great promise as putative anticancer, immunosuppressive, or antiviral drugs. Hence, representative models are needed to study Sec61-dependent protein import into the ER. Microsomal membranes (or microsomes) isolated from dog pancreatic tissue are the primary source of mammalian ER for cell-free in vitro protein translocation research. Here, we demonstrate that for the isolation of microsomal membranes, snap-frozen canine pancreatic tissue can serve as a valuable alternative to freshly isolated organ tissue from euthanized animals. For 17 out of 20 animals, a sufficient yield of microsomes was extracted from defrosted pancreatic tissue. The isolated microsomes contained the essential proteins of the translocation machinery, and proved to be intact as verified by the detection of ER lumenal chaperones. Importantly, 13 out of the 17 microsome samples retained their translocation competence, as reflected by successful in vitro co-translational translocation of wild-type bovine preprolactin. The microsomes supported post-translational modifications of the tested substrates such as signal peptide cleavage and N-linked glycosylation. Furthermore, the tested microsome samples responded well to the translocation inhibitor cyclotriazadisulfonamide in suppressing human CD4 protein translocation into the ER. In conclusion, microsomes isolated from frozen canine pancreatic tissue proved to retain their co-translational translocation functionality that can contribute to our research of Sec61-dependent protein translocation and selective inhibition thereof.

Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland. Current fishing practices involve decisions informed by a combination of personal experience, current data on environmental and oceanographic conditions, reports from other captains, and target species within the constraints of the fishing quota. However, the intricate spatiotemporal dynamics of fish behaviour make it difficult to predict fish stock distributions. Despite technological breakthroughs in fishing vessel data collection, much of the decision-making still relies heavily on subjective judgement, highlighting the need for more robust, data-driven predictive methods. This paper presents CATCH, a convolutional long short-term memory neural network model that forecasts fish stock probability densities over time and space in Icelandic waters to support operational planning and adaptive strategy in fisheries. The framework represents the first utilization of large-scale Icelandic fishing fleet data integrating multidimensional inputs, particularly depth, bottom temperature, salinity, dissolved oxygen and catch data, to produce accurate, multivariate forecasts. The model achieves favourable performance with average RMSE, MAE, WD, and SSI of 4.71 × 10^-3, 1.16 × 10^-3, 0.94 × 10^-3, and 0.955, respectively, for cod, while 6.13 × 10^-3, 1.25 × 10^-3, 1.04 × 10^-3, and 0.949, respectively, across other target species (haddock, saithe, golden redfish, and Greenland halibut). Additionally, Syrjala's test yielded nonsignificant P-values (P > .05) in most cases across lags and forecast horizons, indicating that the predicted and observed distributions are statistically indistinguishable. Its promising results suggest deep learning models have the potential to optimize fisheries operations, enhance sustainability, and support data-driven decision-making.

Cancer cell lines have provided invaluable preclinical mechanistic data for cancer health disparities research. Although there are several studies that detail ancestry inference methods using microarray data, there are none that provide investigators with documentation of ancestry inference methods using sequencing data. Here, we describe our computational workflow for inferring genetic ancestry using either whole genome sequencing (WGS) or RNA-sequencing (RNA-seq) data from cancer cell lines. RNA-seq and WGS datasets were generated from four head and neck cancer cell lines with self-identified race/ethnicity (SIRE) as either White or Black. Our workflow included variant calling and genotype imputation via Illumina DRAGEN pipelines, merging genotyping datasets with the 1000 Genomes Project (1KGP), single nucleotide polymorphism (SNP) filtering via PLINK, and ancestry inference with ADMIXTURE. We encountered challenges in workflow development with SNP filtering and clustering of 1KGP superpopulations. Adjusting stringency of filtering parameters to a window size of 100 kb and r ² threshold of 0.8 resulted in 312,821 SNPs remaining for the RNA-seq dataset and 1,569,578 SNPs remaining for the WGS dataset. Clustering with 1KGP improved with a panel of 291 ancestry informative markers. To estimate proportions of genetic ancestry, we used all filtered SNPs. For the WGS dataset, both clustering and genetic ancestry proportions for each cancer cell line showed concurrence with SIRE. In conclusion, our optimized workflow offers investigators a robust approach for transforming cancer cell line sequencing data to infer genetic ancestry and suggests that WGS datasets are superior to RNA-seq datasets in clustering superpopulations and more accurately estimating genetic ancestry.