Pub Date : 2024-06-20eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae046
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
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-06-20eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae028
Izzy Newsham, Marcin Sendera, Sri Ganesh Jammula, Shamith A Samarajiwa
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-06-19eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae045
Nadia Rashid, Kavaljit H Chhabra
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 in-vitro. 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-06-18eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae043
Viet Thanh Duy Nguyen, Truong Son Hy
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-06-04eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae041
Hwanhee Nam, Esder Lee, Hichang Yang, Kyeyoon Lee, Taeho Kwak, Dain Kim, Hyemin Kim, Mihwa Yang, Younjoo Yang, Seungwan Son, Young-Hyean Nam, Il Minn
Real-time polymerase chain reaction (real-time PCR) is a powerful tool for the precise quantification of nucleic acids in various applications. In cancer management, the monitoring of circulating tumor DNA (ctDNA) from liquid biopsies can provide valuable information for precision care, including treatment selection and monitoring, prognosis, and early detection. However, the rare and heterogeneous nature of ctDNA has made its precise detection and quantification challenging, particularly for ctDNA containing hotspot mutations. We have developed a new real-time PCR tool, PROMER technology, which enables the precise and sensitive detection of ctDNA containing cancer-driven single-point mutations. The PROMER functions as both a PRObe and priMER, providing enhanced detection specificity. We validated PROMER technology using synthetic templates with known KRAS point mutations and demonstrated its sensitivity and linearity of quantification. Using genomic DNA from human cancer cells with mutant and wild-type KRAS, we confirmed that PROMER PCR can detect mutant DNA. Furthermore, we demonstrated the ability of PROMER technology to efficiently detect mutation-carrying ctDNA from the plasma of mice with human cancers. Our results suggest that PROMER technology represents a promising new tool for the precise detection and quantification of DNA containing point mutations in the presence of a large excess of wild-type counterpart.
{"title":"PROMER technology: A new real-time PCR tool enabling multiplex detection of point mutation with high specificity and sensitivity.","authors":"Hwanhee Nam, Esder Lee, Hichang Yang, Kyeyoon Lee, Taeho Kwak, Dain Kim, Hyemin Kim, Mihwa Yang, Younjoo Yang, Seungwan Son, Young-Hyean Nam, Il Minn","doi":"10.1093/biomethods/bpae041","DOIUrl":"https://doi.org/10.1093/biomethods/bpae041","url":null,"abstract":"<p><p>Real-time polymerase chain reaction (real-time PCR) is a powerful tool for the precise quantification of nucleic acids in various applications. In cancer management, the monitoring of circulating tumor DNA (ctDNA) from liquid biopsies can provide valuable information for precision care, including treatment selection and monitoring, prognosis, and early detection. However, the rare and heterogeneous nature of ctDNA has made its precise detection and quantification challenging, particularly for ctDNA containing hotspot mutations. We have developed a new real-time PCR tool, PROMER technology, which enables the precise and sensitive detection of ctDNA containing cancer-driven single-point mutations. The PROMER functions as both a PRObe and priMER, providing enhanced detection specificity. We validated PROMER technology using synthetic templates with known KRAS point mutations and demonstrated its sensitivity and linearity of quantification. Using genomic DNA from human cancer cells with mutant and wild-type KRAS, we confirmed that PROMER PCR can detect mutant DNA. Furthermore, we demonstrated the ability of PROMER technology to efficiently detect mutation-carrying ctDNA from the plasma of mice with human cancers. Our results suggest that PROMER technology represents a promising new tool for the precise detection and quantification of DNA containing point mutations in the presence of a large excess of wild-type counterpart.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141471293","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}
Pub Date : 2024-05-29eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae040
Rishabh Narayanan, William DeGroat, Dinesh Mendhe, Habiba Abdelhalim, Zeeshan Ahmed
Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.
{"title":"<i>IntelliGenes</i>: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.","authors":"Rishabh Narayanan, William DeGroat, Dinesh Mendhe, Habiba Abdelhalim, Zeeshan Ahmed","doi":"10.1093/biomethods/bpae040","DOIUrl":"10.1093/biomethods/bpae040","url":null,"abstract":"<p><p>Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11176709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332027","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}
Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.
{"title":"Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry.","authors":"Junshi Yazaki, Takashi Yamanashi, Shino Nemoto, Atsuo Kobayashi, Yong-Woon Han, Tomoko Hasegawa, Akira Iwase, Masaki Ishikawa, Ryo Konno, Koshi Imami, Yusuke Kawashima, Jun Seita","doi":"10.1093/biomethods/bpae039","DOIUrl":"10.1093/biomethods/bpae039","url":null,"abstract":"<p><p>Mapping protein interaction complexes in their natural state <i>in vivo</i> is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an <i>in vitro</i> pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11180226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332028","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}
Pub Date : 2024-05-28eCollection Date: 2024-01-01DOI: 10.1093/biomethods/bpae036
Maisa I Alkailani
LINE-1 belongs to a family of DNA elements that move to new locations in the genome in a process called "retrotransposition." This is achieved by a copy-and-paste mechanism with the aid of an RNA intermediate. The full-length LINE-1 is responsible for most retrotransposition activity in the human genome. Detecting the active LINE-1 RNA at the endogenous level is challenging due to its small percentage among inactive copies and its different forms of transcripts. Here, we describe a method of designing RNA probes to detect active LINE-1 by northern blotting and use optimized conditions and tools to make the detection practical. This method uses a classical long RNA probe and provides an alternative way to detect LINE-1 RNA using multiple short RNA probes.
{"title":"Northern blotting of endogenous full-length human-specific LINE-1 RNA.","authors":"Maisa I Alkailani","doi":"10.1093/biomethods/bpae036","DOIUrl":"10.1093/biomethods/bpae036","url":null,"abstract":"<p><p>LINE-1 belongs to a family of DNA elements that move to new locations in the genome in a process called \"retrotransposition.\" This is achieved by a copy-and-paste mechanism with the aid of an RNA intermediate. The full-length LINE-1 is responsible for most retrotransposition activity in the human genome. Detecting the active LINE-1 RNA at the endogenous level is challenging due to its small percentage among inactive copies and its different forms of transcripts. Here, we describe a method of designing RNA probes to detect active LINE-1 by northern blotting and use optimized conditions and tools to make the detection practical. This method uses a classical long RNA probe and provides an alternative way to detect LINE-1 RNA using multiple short RNA probes.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11320832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976847","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}
Molecular techniques that recover unknown sequences next to a known sequence region have been widely applied in various molecular studies, such as chromosome walking, identification of the insertion site of transposon mutagenesis, fusion gene partner, and chromosomal breakpoints, as well as targeted sequencing library preparation. Although various techniques have been introduced for efficiency enhancement, searching for relevant single molecular event present in a large-sized genome remains challenging. Here, the optimized ligation-mediated polymerase chain reaction (PCR) method was developed and successfully identified chromosomal breakpoints far away from the exon of the new exon junction without the need for nested PCR. In addition to recovering unknown sequences next to a known sequence region, the high efficiency of the method could also improve the performance of targeted next-generation sequencing (NGS).
{"title":"An optimized ligation-mediated PCR method for chromosome walking and fusion gene chromosomal breakpoints identification.","authors":"Jrhau Lung, Ming-Szu Hung, Chao-Yu Chen, Tsung-Ming Yang, Chin-Kuo Lin, Yu-Hung Fang, Yuan-Yuan Jiang, Hui-Fen Liao, Yu-Ching Lin","doi":"10.1093/biomethods/bpae037","DOIUrl":"10.1093/biomethods/bpae037","url":null,"abstract":"<p><p>Molecular techniques that recover unknown sequences next to a known sequence region have been widely applied in various molecular studies, such as chromosome walking, identification of the insertion site of transposon mutagenesis, fusion gene partner, and chromosomal breakpoints, as well as targeted sequencing library preparation. Although various techniques have been introduced for efficiency enhancement, searching for relevant single molecular event present in a large-sized genome remains challenging. Here, the optimized ligation-mediated polymerase chain reaction (PCR) method was developed and successfully identified chromosomal breakpoints far away from the exon of the new exon junction without the need for nested PCR. In addition to recovering unknown sequences next to a known sequence region, the high efficiency of the method could also improve the performance of targeted next-generation sequencing (NGS).</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11165271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307025","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}
Pub Date : 2024-05-22DOI: 10.1093/biomethods/bpae035
Sofia M da Silva, C. Amaral, Cláudia Malta-Luís, D. Grilo, Américo G. Duarte, Inês Morais, G. Afonso, Nuno Faria, W. Antunes, I. Gomes, R. Sá-Leão, M. Miragaia, Mónica Serrano, C. Pimentel
The gold standard for COVID-19 diagnostic testing relies on RNA extraction from naso/oropharyngeal swab followed by amplification through RT-PCR with fluorogenic probes. While the test is extremely sensitive and specific, its high cost and the potential discomfort associated with specimen collection made it suboptimal for public health screening purposes. In this study, we developed an equally reliable, but cheaper and less invasive alternative test based on a one-step RT-PCR with the DNA-intercalating dye SYBR Green, which enables the detection of SARS-CoV-2 directly from saliva samples or RNA isolated from nasopharyngeal swabs. Importantly, we found that this type of testing can be fine-tuned to discriminate SARS-CoV-2 variants of concern. The saliva RT-PCR SYBR Green test was successfully used in a mass-screening initiative targeting nearly 4500 asymptomatic children under the age of 12. Testing was performed at a reasonable cost, and in some cases, the saliva test outperformed nasopharyngeal rapid antigen tests in identifying infected children. Whole genome sequencing revealed that the antigen testing failure could not be attributed to a specific lineage of SARS-CoV-2. Overall, this work strongly supports the view that RT-PCR saliva tests based on DNA-intercalating dyes represent a powerful strategy for community screening of SARS-CoV-2. The tests can be easily applied to other infectious agents and, therefore, constitute a powerful resource for an effective response to future pandemics.
COVID-19 诊断检测的黄金标准是从鼻/咽拭子中提取 RNA,然后通过含氟探针进行 RT-PCR 扩增。虽然该检测方法灵敏度和特异性极高,但由于其成本高昂,且标本采集过程中可能会引起不适,因此并不适合用于公共卫生筛查。在这项研究中,我们开发了一种同样可靠、但更便宜、侵入性更小的替代检测方法,该方法基于使用 DNA 交联染料 SYBR Green 的一步式 RT-PCR 检测,可直接从唾液样本或从鼻咽拭子中分离的 RNA 中检测 SARS-CoV-2 。重要的是,我们发现这种检测方法可以进行微调,以区分令人担忧的 SARS-CoV-2 变体。唾液 RT-PCR SYBR Green 检测法成功地应用于一项大规模筛查活动,对象是近 4500 名 12 岁以下无症状的儿童。检测成本合理,在某些情况下,唾液检测在识别受感染儿童方面的效果优于鼻咽快速抗原检测。全基因组测序显示,抗原检测失败不能归咎于 SARS-CoV-2 的特定血统。总之,这项工作有力地支持了这样一种观点,即基于 DNA 交联染料的 RT-PCR 唾液检测是社区筛查 SARS-CoV-2 的有力策略。这种检测方法可以很容易地应用于其他传染性病原体,因此是有效应对未来流行病的强大资源。
{"title":"A One-Step Low-Cost molecular test for SARS-CoV-2 detection suitable for community testing using minimally processed saliva","authors":"Sofia M da Silva, C. Amaral, Cláudia Malta-Luís, D. Grilo, Américo G. Duarte, Inês Morais, G. Afonso, Nuno Faria, W. Antunes, I. Gomes, R. Sá-Leão, M. Miragaia, Mónica Serrano, C. Pimentel","doi":"10.1093/biomethods/bpae035","DOIUrl":"https://doi.org/10.1093/biomethods/bpae035","url":null,"abstract":"\u0000 The gold standard for COVID-19 diagnostic testing relies on RNA extraction from naso/oropharyngeal swab followed by amplification through RT-PCR with fluorogenic probes. While the test is extremely sensitive and specific, its high cost and the potential discomfort associated with specimen collection made it suboptimal for public health screening purposes.\u0000 In this study, we developed an equally reliable, but cheaper and less invasive alternative test based on a one-step RT-PCR with the DNA-intercalating dye SYBR Green, which enables the detection of SARS-CoV-2 directly from saliva samples or RNA isolated from nasopharyngeal swabs. Importantly, we found that this type of testing can be fine-tuned to discriminate SARS-CoV-2 variants of concern.\u0000 The saliva RT-PCR SYBR Green test was successfully used in a mass-screening initiative targeting nearly 4500 asymptomatic children under the age of 12. Testing was performed at a reasonable cost, and in some cases, the saliva test outperformed nasopharyngeal rapid antigen tests in identifying infected children. Whole genome sequencing revealed that the antigen testing failure could not be attributed to a specific lineage of SARS-CoV-2.\u0000 Overall, this work strongly supports the view that RT-PCR saliva tests based on DNA-intercalating dyes represent a powerful strategy for community screening of SARS-CoV-2. The tests can be easily applied to other infectious agents and, therefore, constitute a powerful resource for an effective response to future pandemics.","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}