Biology Methods and Protocols最新文献

{"title":"Simple protocol for combined extraction of exocrine secretions and RNA in small arthropods.","authors":"David Fröhlich, Michaela Bodner, Günther Raspotnig, Christoph Hahn","doi":"10.1093/biomethods/bpae054","DOIUrl":"10.1093/biomethods/bpae054","url":null,"abstract":"<p><p>The integration of data from multiple sources and analytical techniques to obtain novel insights and answer challenging questions is a hallmark of modern science. In arthropods, exocrine secretions may act as pheromones, defensive substances, antibiotics, as well as surface protectants, and as such they play a crucial role in ecology and evolution. Exocrine chemical compounds are frequently characterized by gas chromatography-mass spectrometry. Technological advances of recent years now allow us to routinely characterize the total gene complement transcribed in a particular biological tissue, often in the context of experimental treatment, via RNAseq. We here introduce a novel methodological approach to successfully characterize exocrine secretions <i>and</i> full transcriptomes of one and the same individual of oribatid mites. We found that chemical extraction prior to RNA extraction had only minor effects on the total RNA integrity. De novo transcriptomes obtained from such combined extractions were of comparable quality to those assembled for samples that were subject to RNA extraction only, indicating that combined chemical/RNA extraction is perfectly suitable for phylotranscriptomic studies. However, in-depth analysis of RNA expression analysis indicates that chemical extraction prior to RNAseq may affect transcript degradation rates, similar to the effects reported in previous studies comparing RNA extraction protocols. With this pilot study, we demonstrate that profiling chemical secretions and RNA expression levels from the same individual is methodologically feasible, paving the way for future research to understand the genes and pathways underlying the syntheses of biogenic chemical compounds. Our approach should be applicable broadly to most arachnids, insects, and other arthropods.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae054"},"PeriodicalIF":2.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.","authors":"Camilla Mapstone, Helen Hunter, Daniel Brison, Julia Handl, Berenika Plusa","doi":"10.1093/biomethods/bpae052","DOIUrl":"10.1093/biomethods/bpae052","url":null,"abstract":"<p><p>Demand for in vitro fertilization (IVF) treatment is growing; however, success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in pre-implantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited datasets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae052"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The landscape of RNA 3D structure modeling with transformer networks.","authors":"Sumit Tarafder, Rahmatullah Roche, Debswapna Bhattacharya","doi":"10.1093/biomethods/bpae047","DOIUrl":"10.1093/biomethods/bpae047","url":null,"abstract":"<p><p>Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae047"},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11244692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning of cellular metabolic rewiring.","authors":"Joao B Xavier","doi":"10.1093/biomethods/bpae048","DOIUrl":"10.1093/biomethods/bpae048","url":null,"abstract":"<p><p>Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce <i>MetaboLiteLearner</i>, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, <i>MetaboLiteLearner</i> predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae048"},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput evaluation of hemolytic activity through precise measurement of colony and hemolytic zone sizes of engineered <i>Bacillus subtilis</i> on blood agar.","authors":"Takahiro Bamba, Rina Aoki, Yoshimi Hori, Shu Ishikawa, Ken-Ichi Yoshida, Naoaki Taoka, Shingo Kobayashi, Hisashi Yasueda, Akihiko Kondo, Tomohisa Hasunuma","doi":"10.1093/biomethods/bpae044","DOIUrl":"10.1093/biomethods/bpae044","url":null,"abstract":"<p><p>Biosurfactants have remarkable characteristics, such as environmental friendliness, high safety, and excellent biodegradability. Surfactin is one of the best-known biosurfactants produced by <i>Bacillus subtilis</i>. Because the biosynthetic pathways of biosurfactants, such as surfactin, are complex, mutagenesis is a useful alternative to typical metabolic engineering approaches for developing high-yield strains. Therefore, there is a need for high-throughput and accurate screening methods for high-yield strains derived from mutant libraries. The blood agar lysis method, which takes advantage of the hemolytic activity of biosurfactants, is one way of determining their concentration. This method includes inoculating microbial cells onto blood-containing agar plates, and biosurfactant production is assessed based on the size of the hemolytic zone formed around each colony. Challenges with the blood agar lysis method include low experimental reproducibility and a lack of established protocols for high-throughput screening. Therefore, in this study, we investigated the effects of the inoculation procedure and media composition on the formation of hemolytic zones. We also developed a workflow to evaluate the number of colonies using robotics. The results revealed that by arranging colonies at appropriate intervals and measuring the areas of colonies and hemolytic rings using image analysis software, it was possible to accurately compare the hemolytic activity among several colonies. Although the use of the blood agar lysis method for screening is limited to surfactants exhibiting hemolytic activity, it is believed that by considering the insights gained from this study, it can contribute to the accurate screening of strains with high productivity.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae044"},"PeriodicalIF":2.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KCL TEST: an open-source inspired asymptomatic SARS-CoV-2 surveillance programme in an academic institution.","authors":"Joana Reis de Andrade, Edward Scourfield, Shilpa Lekhraj Peswani-Sajnani, Kate Poulton, Thomas Ap Rees, Paniz Khooshemehri, George Doherty, Stephanie Ong, Iustina-Francisca Ivan, Negin Goudarzi, Isaac Gardiner, Estelle Caine, Thomas J A Maguire, Daniel Leightley, Luis Torrico, Alex Gasulla, Angel Menendez-Vazquez, Ana Maria Ortega-Prieto, Suzanne Pickering, Jose M Jimenez-Guardeño, Rahul Batra, Sona Rubinchik, Aaron V F Tan, Amy Griffin, David Sherrin, Stelios Papaioannou, Celine Trouillet, Hannah E Mischo, Victoriano Giralt, Samantha Wilson, Martin Kirk, Stuart J D Neil, Rui Pedro Galao, Jo Martindale, Charles Curtis, Mark Zuckerman, Reza Razavi, Michael H Malim, Rocio T Martinez-Nunez","doi":"10.1093/biomethods/bpae046","DOIUrl":"10.1093/biomethods/bpae046","url":null,"abstract":"<p><p>Rapid and accessible testing was paramount in the management of the COVID-19 pandemic. Our university established KCL TEST: a SARS-CoV-2 asymptomatic testing programme that enabled sensitive and accessible PCR testing of SARS-CoV-2 RNA in saliva. Here, we describe our learnings and provide our blueprint for launching diagnostic laboratories, particularly in low-resource settings. Between December 2020 and July 2022, we performed 158277 PCRs for our staff, students, and their household contacts, free of charge. Our average turnaround time was 16 h and 37 min from user registration to result delivery. KCL TEST combined open-source automation and in-house non-commercial reagents, which allows for rapid implementation and repurposing. Importantly, our data parallel those of the UK Office for National Statistics, though we detected a lower positive rate and virtually no delta wave. Our observations strongly support regular asymptomatic community testing as an important measure for decreasing outbreaks and providing safe working spaces. Universities can therefore provide agile, resilient, and accurate testing that reflects the infection rate and trend of the general population. Our findings call for the early integration of academic institutions in pandemic preparedness, with capabilities to rapidly deploy highly skilled staff, as well as develop, test, and accommodate efficient low-cost pipelines.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae046"},"PeriodicalIF":2.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.","authors":"Izzy Newsham, Marcin Sendera, Sri Ganesh Jammula, Shamith A Samarajiwa","doi":"10.1093/biomethods/bpae028","DOIUrl":"10.1093/biomethods/bpae028","url":null,"abstract":"<p><p>Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae028"},"PeriodicalIF":2.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11186673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors.","authors":"Nadia Rashid, Kavaljit H Chhabra","doi":"10.1093/biomethods/bpae045","DOIUrl":"10.1093/biomethods/bpae045","url":null,"abstract":"<p><p>Sensing, transport, and utilization of glucose is pivotal to the maintenance of energy homeostasis in animals. Although transporters involved in mobilizing glucose across different cellular compartments are fairly well known, the receptors that bind glucose to mediate its effects independently of glucose metabolism remain largely unrecognized. Establishing precise and reproducible methods to identify glucose receptors in the brain or other peripheral organs will pave the way for comprehending the role of glucose signaling pathways in maintaining, regulating, and reprogramming cellular metabolic needs. Identification of such potential glucose receptors will also likely lead to development of effective therapeutics for treatment of diabetes and related metabolic disorders. Commercially available biotin or radiolabeled glucose conjugates have low molecular weight; therefore, they do not provide enough sensitivity and density to isolate glucose receptors. Here, we describe a protocol to isolate, identify, and verify glucose-binding receptor/s using high molecular weight glucose (or other carbohydrate) conjugates. We have produced 30 kDa glucose- (or other carbohydrate-) biotin-polyacrylamide (PAA) conjugates with mole fractions of 80:5:15% respectively. These conjugates are used with biotin-streptavidin biochemistry, In-cell ELISA, and surface plasmon resonance (SPR) methods to isolate, identify, and verify glucose- or carbohydrate-binding receptors. We first demonstrate how streptavidin-coated magnetic beads are employed to immobilize glucose-biotin-PAA conjugates. Then, these beads are used to enrich and isolate glucose-binding proteins from tissue homogenates or from single-cell suspensions. The enriched or isolated proteins are subjected to mass spectrometry/proteomics to reveal the identity of top candidate proteins as potential glucose receptors. We then describe how the In-cell ELISA method is used to verify the interaction of glucose with its potential receptor through stable expression of the receptor <i>in-vitro</i>. We further demonstrate how a highly sensitive SPR method can be used to measure the binding kinetics of glucose with its receptor. In summary, we describe a protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors using magnetic beads, In-cell ELISA, and SPR. This protocol will form the future basis of studying glucose or carbohydrate receptor signaling pathways in health and in disease.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae045"},"PeriodicalIF":2.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11222014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal pretraining for unsupervised protein representation learning.","authors":"Viet Thanh Duy Nguyen, Truong Son Hy","doi":"10.1093/biomethods/bpae043","DOIUrl":"10.1093/biomethods/bpae043","url":null,"abstract":"<p><p>Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein-ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae043"},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of plate reader-based indooxine and Nessler protocols for monitoring L-asparaginase serum activity in childhood acute lymphoblastic leukaemia.","authors":"Bozhidar Vergov, Yordan Sbirkov, Danail Minchev, Tatyana Todorova, Alexandra Baldzhieva, Hasan Burnusuzov, Мariya I Spasova, Victoria Sarafian","doi":"10.1093/biomethods/bpae042","DOIUrl":"10.1093/biomethods/bpae042","url":null,"abstract":"<p><p>Monitoring the blood serum activity of L-asparaginase in children with acute lymphoblastic leukaemia (ALL) has been highly recommended to detect enzyme inactivation that can cause relapse and to avoid unwanted toxicity. Nevertheless, perhaps at least partially due to the lack of clinically approved commercially available kits or standardized and independently reproduced and validated in-house protocols, laboratory assay-based determination of the optimal doses of L-asparaginase is not carried out routinely. In this study, we adapted previously published protocols for two plate reader-based colorimetric methods, indooxine and Nessler, to measure asparaginase activity. Mock samples with dilutions of the enzyme for initial optimization steps, and patient samples were used as a proof of principle and to compare the two protocols. For the first time the indooxine and the Nessler methods are adapted for a plate reader and L-asparaginase serum activity levels are compared by both protocols. Passing-Bablok and Bland-Altman's statistical analyses found very little difference, strong correlation (<i>r</i> = 0.852), and bias of only 6% between the data from the two methods when used for fresh patient samples. Furthermore, we demonstrate that the Nessler method could also be applied for frozen sera as the results, compared to fresh samples, showed little difference, strong correlation (<i>r</i> = 0.817), and small bias (9%). We successfully adapted and validated two methods for measuring L-asparaginase activity in cALL and provided the most detailed description to date on how to reproduce and implement them in other clinical laboratories.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae042"},"PeriodicalIF":2.5,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}