Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
AI will help reduce time for drug development especially in early phase of discovery but eventually help in all phases
Ganhui: for drug regulators might be more amenable to AI in clinical trials; AI may be used differently by clinicians
nonprofit in Philadelphia using AI to repurpose drugs (this site has posted on this and article will be included here)
Ganhui: top challenge of AI in Pharma; rapid evolution of AI and have to have core understanding of your needs and dependencies; realistic view of what can be done; AI has to have iterative learning; also huge vertical challenge meaning how can we allign the use of AI through the healthcare vertical layer chain like clinicians, payers, etc.
Ganhui sees a challenge for health companies to understand how to use AI in business to technology; AI in AI companies is different need than AI in healthcare companies
95% of AI projects not successful because most projects are very discrete use
2:00-2:20
Building Precision Oncology Infrastructure in Low- and Middle-Income Countries
globally 60 precision initiatives but there really are because many in small countries
three out of five individuals in India die of cancer
precision medicine is a must and a hub and spoke model is needed in these places; Italy does this hub and spoke; spokes you enable the small places and bring them into the network so they know how and have access to precision medicine
in low income countries the challenge starts with biopsy: then diagnosis and biomarker is issue; then treatment decision a problem as they may not have access to molecular tumor boards
prevention is always a difficult task in LMICs (low income)
you have ten times more patients in India than in US (triage can be insurmountable)
ICGA Foundation: Indian Cancer Genome Atlas
in India mutational frequencies vary with geographical borders like EGFR mutations or KRAS mutations
genomic landscape of ovarian cancer in India totally different than in TCGA data
even different pathways are altered in ovarian cancer seen in North America than in India
MAY mean that biomarker panels need to be adjusted based on countries used in
the molecular data has to be curated for the India cases to be submitted to a tumor board
twenty diagnostic tests in market like TruCheck for Indian market; uses liquid biopsy
they are also tailoring diagnostic and treatment for India getting FDA fast track approvals
2:20-2:40
Co-targeting KIT/PDGRFA and Genomic Integrity in Gastrointestinal Stromal Tumors
Lori Rink, PhD, Associate Professor, Fox Chase Cancer Center
GIST are most common nesychymal tumor in GI tract
used to be misdiagnosed; was considered a leimyosarcoma
very asymptomatic tumors and not good prognosis
very refractory to genotoxic therapies
RTK KIT/PDGFRA gain of function mutations
Gleevec imatinib for unresectable GIST however vast majority of even responders become resistant to therapy and cancer returns
there is a mutation map for hotspot mutations and sensitivity for gleevec
however resistance emerged to ripretinib; in ATP binding pocket
over treatment get a polyclonal resistance
performed a kinome analysis; Wee1 looked like a potential target
mouse studies (80 day) showed good efficacy
avapiritinib ahs some neurotox and used in PDGFRA mut GIST model which is resistant to imitinib
but if use Wee1 inhibitor with TKI can lower dose of avapiritinib
cotargeting KIT/PDGFRA and WEE1 increases replicative stress
they are using PDX models to test these combinations
Dr. El-Diery welcomes all to this joint symposium with Advancing Precision Medicine and the Win Consortium, which he is currently the head of. More in the WIN Consortium: (Worldwide Innovation Network Consortium in Precision Oncology). The WIN Network is involved in setting up internationalclinical tumor board collaboration
WIN was formed on the premise that we can accomplish more together than each organization can achieve working alone. We aim to improve cancer patients’ survival and quality of life. View WIN’s history and unique attributes:
WIN members collaboratively design and carry out global studies designed to achieve breakthroughs for patients worldwide. Our distinguished Scientific Advisory Board oversees WIN studies. Current trials include:
They guide WIN’s strategic, operational, and scientific direction.
OrganizationThe WIN Consortium is organized in the following groups who collectively work together to achieve WIN’s common goals
Nigel Russell, Founder and CEO, Advancing Precision Medicine
Christopher P. Molineaux, President & Chief Executive Officer, Life Science Pennsylvania
Life Sciences Pennsylvania (LSPA) is the statewide trade association for the commonwealth’s life sciences industry. Founded in 1989, LSPA works to ensure Pennsylvania has a business and public policy climate that makes the commonwealth the most attractive location to open and operate a life sciences company. Our membership is comprised of organizations statewide, representing the entire ecosystem of the life sciences: research institutions, biotechnology, medical device, diagnostic, pharmaceutical, and investment entities, along with service providers who support the industry. Together, we unify Pennsylvania’s innovators to make the Commonwealth a global life sciences leader.
As president & CEO of Life Sciences Pennsylvania, Christopher Molineaux serves as the chief advocate and spokesman for the life sciences industry that calls Pennsylvania home. Molineaux oversees the strategic direction for the association, assuring Life Sciences Pennsylvania continues to be the catalyst that makes Pennsylvania the top location for life sciences companies.
Molineaux brings to Life Sciences Pennsylvania more than 25 years of experience in the bio-pharmaceutical and health care industries, with front-line experience in developing and executing strategies to navigate a shifting economic and political environment.
9:00-9:40
Keynote Lecture – WIN Consortium
Targeting the Achilles’ Heel of Cancer: Synthetic Lethality and Hypoxia in Precision Oncology
William Kaelin was born in New York City. He studied chemistry and mathematics at Duke University in Durham, North Carolina, and received his doctor of medicine degree there in 1982. He then did his residency at Johns Hopkins University in Baltimore, Maryland. In 2002 he became a professor at Harvard Medical School in Cambridge, Massachusetts.
Work
Animals need oxygen for the conversion of food into useful energy. The importance of oxygen has been understood for centuries, but how cells adapt to changes in levels of oxygen has long been unknown. William Kaelin, Peter Ratcliffe, and Gregg Semenza discovered how cells can sense and adapt to changing oxygen availability. During the 1990s they identified a molecular machinery that regulates the activity of genes in response to varying levels of oxygen. The discoveries may lead to new treatments of anemia, cancer and many other diseases.
TRACK 1 204BC
WIN SYMPOSIUM
MULTI-OMICS
9:40 – 10:40
SESSION 1
From Base Pairs To Better Care:
AI and Omics in Precision Oncology
9:40-10:00
Multi-Omic Profiling and Clinical Decision Support in Precision Oncology
David Spetzler, PhD, MBA, MS, President, Caris Life Sciences
10:00-10:20
Integrating Omics and AI for Next-Gen Precision Oncology
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
10:20-10:40
Real-World Data and AI in Precision Oncology: Making Data Work for Patients – Q&A
MODERATOR: Jeff Elton, PhD, Vice Chairman, Founding CEO
ConcertAI
PANELISTS: David Spetzler, PhD, MBA, MS, President, Caris Life Sciences
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
Daryl Pritchard, PhD, Interim President, Personalized Medicine Coalition
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
SESSION 3
The Shifting Landscape:
Tumor Plasticity and Resistance
12:00-12:20
Mathematical and Evolutionary Modeling in Precision Radiation Oncology
Jacob Scott, MD, DPhil, Professor and Staff Physician-Scientist, CWRU School of Medicine and Cleveland Clinic
12:20-12:40
Plasticity and Persistence: The Role of EMT in Cancer Progression and Therapy Resistance
Sendurai A. Mani, PhD, Professor of Pathology and Laboratory Medicine, Brown University; Associate Director of Translational Oncology, Brown University Legorreta Cancer Center
12:40-1:00
Targeting Molecularly Defined Subsets: Challenges in Translational Oncology
Benedito A. Carneiro, MD, MS, Director, Clinical Research Director, Cancer Drug Development; Associate Director, Division of Hematology/Oncology
Legorreta Cancer Center, Brown University Health
Live Conference Coverage: International Dialogue in Gynecological Oncology, From Bench to Bedside, Ovarian Cancer
Reporter: Stephen J. Williams, Ph.D.
Join Live on Wednesday May 22, 2024 for an international discussion on the current state of ovarian cancer diagnostics and therapeutics, and potential therapies and biomarkers, and biotargets. Topics including potential new molecular targets for development of ovarian therapeutics, current changes in ovarian cancer clinical treatment protocols, chemo-resistance, and the use of Artificial Intelligence (AI) in the diagnosis and treatment of cancer will be discussed.
10/15.10 We Have Never Been Only Human: a new perspective to defeat ovarian cancer (C. Martinelli)
Molecular Section
20/15.20 DNA Repair mechanisms: understanding their role in cancer development and chemoresistance (L. Alfano)
35/15.35 Progranulins: a new target for oncological treatment (A. Morrione)
50/15.50 Modulation of gene expression and its applications (M. Cuomo)
10.05/16.05 Commanding the cell cycle: the role of CDKs (S.R. Burk
10.20/16.20 Drug development from nature (M. D’Angelo
Clinical Section
05/17.05 Core principles of Radiologic Diagnosis & Staging in Ovarian Cancer(A. Blandino)
20/17.20 Key Indications for Nuclear Medicine in Ovarian Cancer (S. Baldari)
35/17.35 Cutting Edge Decision: Understanding Surgical Indications and Outcomes in Ovarian Cancer (A. Ercoli)
50/17.50 Gold Standard in Oncology for Ovarian Cancer (N. Silvestris)
12.05/18.05 Role of Radiotherapy in Ovarian Cancer (S. Pergolizzi)
Conclusion
12.20/18.20 AI Applied to medical science (V. Carnevale)
Speakers
– Professor Alfredo Blandino: Professor Blandino holds the esteemed positions of Head of school of Radiology and director of the department of radiology at the University of Messina. He has made significant contributions to diagnostic imaging with over hundreds of publications to his name, Professor Blandino’s work exemplifies excellence and innovation in radiology.
– Professor Alfredo Ercoli, serves as the Director of the Department of Gynecology and Obstetrics at the “G. Martino” University Hospital in Messina. He is also head of school of gynecology and obstetrics at Messina University. Starting his research in France with studies on pelvic anatomy that became a cornerstone in medical literature, He is a pioneer in advanced gynecologic surgery, including laparoscopic and robotic procedures, having performed over thousands of surgical interventions. His research focuses on gynecologic oncology, advanced gynecologic surgery, and endometriosis, urogynecology. Professor Ercoli’s dedication to education and his numerous publications have significantly advanced the field of gynecology.
–Professor Sergio Baldari, an eminent figure in nuclear medicine. Professor Baldari is the Director of the department of nuclear medicine and head of school of nuclear medicine at the University of Messina. He has authored or co-authored over 500 publications, with a focus on diagnostic imaging and the use of PET and radiopharmaceuticals in cancer treatment. His leadership and expertise have been recognized through various prestigious positions and awards within the medical community.
– Professor Nicola Silvestris is the Director of UOC Oncologia Medica at the University of Messina. His extensive research in cancer, has led to over 360 peer-reviewed publications. Professor Silvestris has made significant contributions to translational research and the development of guidelines for managing complex oncological conditions. His work continues to shape the future of cancer treatment.
–Professor Stefano Pergolizzi, a leading expert in radiation oncology. Professor Pergolizzi serves as the Director of the department of radiotherapy and head of the school of radiotherapya at the University of Messina. He is also the president of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) His research focuses on advanced radiotherapy techniques for cancer treatment. With a career spanning several decades, Professor Pergolizzi has published numerous papers and has been instrumental in developing innovative therapeutic approaches. His dedication to patient care and education is exemplary.
Margherita D’angelo: Graduated in Molecular Biology with honors from the Federico II University of Naples.
Third year intern in Food Science at the Luigi Vanvitelli University of Naples.
Research intern in Molecular oncology with the project of developing novel drugs starting from food waste at the Sbarro Institute for Cancer Research and Molecular Medicine at Temple University, Philadelphia (USA), directed by Dr A. Giordano.
Dr. Carnevale is an Associate Professor in the Institute for Computational Molecular Science in the College of Science & Technology, Temple University. He holds multiple NIH RO1 and NSF grants. Vincenzo Carnevale received B.Sc. and M.Sc. degrees in Physics from the University of Pisa and a PhD from SISSA – Scuola Internazionale Superiore di Studi Avanzati in Trieste, Italy. The Carnevale research group uses statistical physics and machine learning approaches to investigate sequence-structure-function relations in proteins. A central theme of the group’s research is how interactions give rise to collective phenomena and complex emergent behaviors. At the level of genes, the group is interested in epistasis – the complex entanglement phenomenon that causes amino acids to evolve in a concerted fashion – and how this shapes molecular evolution. At the cellular level, the group investigates how intermolecular interactions drive biomolecules toward self-organization and pattern formation. A long-term goal of the group is understanding the molecular underpinnings of electrical signaling in excitable cells. Toward these goals, the group applies and actively develops an extensive arsenal of theoretical and computational approaches including statistical (mean)field theories, Monte Carlo and molecular dynamics simulations, statistical inference of generative models, and deep learning.
Professor Andrea Morrione, Ph.D: Research Associate Professor, CST Temple University; After his studies in Biochemistry at Universita’ degli Studi Milano, Milan Italy, Dr. Morrione moved to USA in 1993 and has been working in the field of cancer biology since his postdoctoral training at the Kimmel Cancer Institute, Thomas Jefferson University, Philadelphia, PA in the laboratory of Dr. Renato Baserga, one of the leading experts in IGF-IR oncogenic signaling. In 1997 Dr. Morrione joined the Faculty of Thomas Jefferson University in the Department of Microbiology. In 2002 after receiving an NIH/NIDDK Career Development Award Dr. Morrione joined the Department of Urology at Jefferson where from 2008 to 2018 serves as the Director for Urology Basic Science and Associate Professor. Dr. Morrione joined the Department of Biology and the Sbarro Institute for Cancer Research and Molecular Medicine and Center for Biotechnology as Associate Professor of Research, and he is currently professor of Research and Deputy Director of the Sbarro Institute for Cancer Research and Molecular Medicine and Center for Biotechnology. He is a full member of the AACR.
Canio Martinelli, M.D.: Dr. Marinelli received his MD from Catholic University of the Sacred Heart in Rome, Visiting researcher at SHRO Temple University in Philadelphia, PhD candidate in Translational Molecular Medicine and Surgery & GYN-OB resident at UNIME. He has published numerous clinical papers in gynecologic oncology, risk reduction, and therapy and, most recently investigating clinical utilities of generative AI in gynecologic oncology.
Sharon Burk, Sharon Burk is a PhD student with Professor Antonio Giordano at the University of Siena, Italy in the department of Medical Biotechnologies, studying the role of Cyclin Dependent Kinase 10 in Triple Negative Breast Cancer. She received her Bachelor’s of Arts Degree from the University of California, Berkeley with a double major in molecular and cell biology and Italian studies. She is a member of AACR.
The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT
Curator: Stephen J. Williams, Ph.D.
Chatbots are being used in multiple interdisciplanary areas in research and medicine and so it was a natural progression to incorporate artificial intelligence (AI), natural language processing (NLP), and chatbot technology like ChatGPT into bioinformatic analysis.
“In domains like informatics, management and marketing, media and communication science, languages and philosophy, psychology and sociology, engineering, design, and human-computer interaction, the fast expanding body of chatbot study is clearly interdisciplinary.
The field of bioinformatics is a natural fit for incorporting this technology. The curated information below shows some use examples of this technology in bioinformatics related to cancer research.
How ChatGPT can be used in bioinformatics research?
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model that was designed to generate human-like text in a conversational setting. It is not directly related to bioinformatics, which is the field of study that deals with the storage, retrieval, and analysis of biological data, particularly in the context of genetics and genomics. However, ChatGPT could potentially be used in a bioinformatics context as a tool for generating natural language descriptions of genetic or genomic data, or for generating responses to queries about such data.
For example, a researcher could use ChatGPT to generate descriptions of genetic variants or gene expression patterns based on input data. This could be useful for generating summaries of results for reports or papers, or for generating explanations of complex concepts for non-expert audiences. ChatGPT could also be used to generate responses to queries about genetic or genomic data, such as “What is the function of gene X?” or “What are the potential consequences of variant Y?”
It’s worth noting that ChatGPT is just one of many tools and techniques that can be used in bioinformatics, and it is unlikely to be the most suitable or effective option in every situation. There are many specialized tools and resources available for working with biological data, and it is often necessary to use a combination of these tools to fully analyze and understand the data.
Generating descriptions of genetic or genomic data: ChatGPT could be used to generate natural language descriptions of genetic or genomic data based on input data. For example, suppose a researcher has a dataset containing information about gene expression levels in different tissues. The researcher could use ChatGPT to generate a description of the data, such as:
“Gene X is highly expressed in the liver and kidney, with moderate expression in the brain and heart. Gene Y, on the other hand, shows low expression in all tissues except for the lung, where it is highly expressed.”
Thereby ChatGPT, at its simplest level, could be used to ask general questions like “What is the function of gene product X?” and a ChatGPT could give a reasonable response without the scientist having to browse through even highly curated databases lie GeneCards or UniProt or GenBank. Or even “What are potential interactors of Gene X, validated by yeast two hybrid?” without even going to the curated InterActome databases or using expensive software like Genie.
Summarizing results: ChatGPT could be used to generate summaries of results from genetic or genomic studies. For example, a researcher might use ChatGPT to generate a summary of a study that found a association between a particular genetic variant and a particular disease. The summary might look something like this:
“Our study found that individuals with the variant form of gene X are more likely to develop disease Y. Further analysis revealed that this variant is associated with changes in gene expression that may contribute to the development of the disease.”
It’s worth noting that ChatGPT is just one tool that could potentially be used in these types of applications, and it is likely to be most effective when used in combination with other bioinformatics tools and resources. For example, a researcher might use ChatGPT to generate a summary of results, but would also need to use other tools to analyze the data and confirm the findings.
ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model that is designed for open-domain conversation. It is not specifically designed for generating descriptions of genetic variants or gene expression patterns, but it can potentially be used for this purpose if you provide it with a sufficient amount of relevant training data and fine-tune it appropriately.
To use ChatGPT to generate descriptions of genetic variants or gene expression patterns, you would first need to obtain a large dataset of examples of descriptions of genetic variants or gene expression patterns. You could use this dataset to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants or gene expression patterns.
Here’s an example of how you might use ChatGPT to generate a description of a genetic variant:
First, you would need to pre-process your dataset of descriptions of genetic variants to prepare it for use with ChatGPT. This might involve splitting the descriptions into individual sentences or phrases, and encoding them using a suitable natural language processing (NLP) library or tool.
Next, you would need to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants. This could involve using a tool like Hugging Face’s Transformers library to load the ChatGPT model and your pre-processed dataset, and then training the model on the task of generating descriptions of genetic variants using an appropriate optimization algorithm.
Once the model has been fine-tuned, you can use it to generate descriptions of genetic variants by providing it with a prompt or seed text and asking it to generate a response. For example, you might provide the model with the prompt “Generate a description of a genetic variant associated with increased risk of breast cancer,” and ask it to generate a response. The model should then generate a description of a genetic variant that is associated with increased risk of breast cancer.
It’s worth noting that generating high-quality descriptions of genetic variants or gene expression patterns is a challenging task, and it may be difficult to achieve good results using a language model like ChatGPT without a large amount of relevant training data and careful fine-tuning.
To train a language model like chatGPT to extract information about specific genes or diseases from research papers, you would need to follow these steps:
Gather a large dataset of research papers that contain information about the specific genes or diseases you are interested in. This dataset should be diverse and representative of the types of papers you want the model to be able to extract information from.
Preprocess the text data in the research papers by tokenizing the text and creating a vocabulary. You may also want to consider lemmatizing or stemming the text to reduce the dimensionality of the dataset.
Train the language model on the preprocessed text data. You may want to fine-tune a pre-trained model such as chatGPT on your specific dataset, or you can train a new model from scratch.
ChatGPT could also be useful for sequence analysis
A few examples of sequence analysis a ChatGPT could be useful include:
Protein structure
Identifying functional regions of a protein
Predicting protein-protein interactions
Identifying protein homologs
Generating Protein alignments
All this could be done without having access to UNIX servers or proprietary software or knowing GCG coding
ChatGPT in biomedical research
There are several potential ways that ChatGPT or other natural language processing (NLP) models could be applied in biomedical research:
Text summarization: ChatGPT or other NLP models could be used to summarize large amounts of text, such as research papers or clinical notes, in order to extract key information and insights more quickly.
Data extraction: ChatGPT or other NLP models could be used to extract structured data from unstructured text sources, such as research papers or clinical notes. For example, the model could be trained to extract information about specific genes or diseases from research papers, and then used to create a database of this information for further analysis.
Literature review: ChatGPT or other NLP models could be used to assist with literature review tasks, such as identifying relevant papers, extracting key information from papers, or summarizing the main findings of a group of papers.
Predictive modeling: ChatGPT or other NLP models could be used to build predictive models based on large amounts of text data, such as electronic health records or research papers. For example, the model could be trained to predict the likelihood of a patient developing a particular disease based on their medical history and other factors.
It’s worth noting that while NLP models like ChatGPT have the potential to be useful tools in biomedical research, they are only as good as the data they are trained on, and it is important to carefully evaluate the quality and reliability of any results generated by these models.
ChatGPT in text mining of biomedical data
ChatGPT could potentially be used for text mining in the biomedical field in a number of ways. Here are a few examples:
Extracting information from scientific papers: ChatGPT could be trained on a large dataset of scientific papers in the biomedical field, and then used to extract specific pieces of information from these papers, such as the names of compounds, their structures, and their potential uses.
Generating summaries of scientific papers: ChatGPT could be used to generate concise summaries of scientific papers in the biomedical field, highlighting the main findings and implications of the research.
Identifying trends and patterns in scientific literature: ChatGPT could be used to analyze large datasets of scientific papers in the biomedical field and identify trends and patterns in the data, such as emerging areas of research or common themes among different papers.
Generating questions for further research: ChatGPT could be used to suggest questions for further research in the biomedical field based on existing scientific literature, by identifying gaps in current knowledge or areas where further investigation is needed.
Generating hypotheses for scientific experiments: ChatGPT could be used to generate hypotheses for scientific experiments in the biomedical field based on existing scientific literature and data, by identifying potential relationships or associations that could be tested in future research.
PLEASE WATCH VIDEO
In this video, a bioinformatician describes the ways he uses ChatGPT to increase his productivity in writing bioinformatic code and conducting bioinformatic analyses.
He describes a series of uses of ChatGPT in his day to day work as a bioinformatian:
Using ChatGPT as a search engine: He finds more useful and relevant search results than a standard Google or Yahoo search. This saves time as one does not have to pour through multiple pages to find information. However, a caveat is ChatGPT does NOT return sources, as highlighted in previous postings on this page. This feature of ChatGPT is probably why Microsoft bought OpenAI in order to incorporate ChatGPT in their Bing search engine, as well as Office Suite programs
ChatGPT to help with coding projects: Bioinformaticians will spend multiple hours searching for and altering open access available code in order to run certain function like determining the G/C content of DNA (although there are many UNIX based code that has already been established for these purposes). One can use ChatGPT to find such a code and then assist in debugging that code for any flaws
ChatGPT to document and add coding comments: When writing code it is useful to add comments periodically to assist other users to determine how the code works and also how the program flow works as well, including returned variables.
One of the comments was interesting and directed one to use BIOGPT instead of ChatGPT
0:54 oh dear. You cannot use chatgpt like that in Bioinformatics as it is rn without double checking the info from it. You should be using biogpt instead for paper summarisation. ChatGPT goes for human-like responses over precise information recal. It is quite good for debugging though and automating boring awkward scripts
The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.
The abstract from the paper is the following:
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
Tips:
BioGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.
This model was contributed by kamalkraj. The original code can be found here.
BioGPT is MIT-licensed. The license applies to the pre-trained models as well.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
As of right now this does not seem Open Access, however a sign up is required!
We provide our pre-trained BioGPT model checkpoints along with fine-tuned checkpoints for downstream tasks, available both through URL download as well as through the Hugging Face 🤗 Hub.
Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY.Brief Bioinform. 2022 Nov 19;23(6):bbac409. doi: 10.1093/bib/bbac409.PMID: 36156661
We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. …Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fl …
Here they show the advantages of BioGPT over BioBERT and ChatGPT in an example of text extraction and question answer
Table 7. Example of generated text
Input Model Text
Bicalutamide GPT-2 Bicalutamide, a synthetic bicalutamide derivative, was found to increase the cellular proliferation
of C. elegans cells. This effect was not observed with the other synthetic bicalutamide derivatives. This is in line with our previous work which showed that bicalutamide increases the proliferation of C. elegans cells but not in the presence of synthetic bicalutamide.
BioGPT Bicalutamide (Casodex) is an androgen receptor (AR) antagonist approved for the treatment of metastatic castration-resistant prostate cancer (mCRPC) in patients who have progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of early-stage prostate cancer in men who have not progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of metastatic castration-sensitive prostate cancer (mCSPC) in men who have not progressed on or are ineligible for docetaxel chemotherapy.
Janus kinase 3 (JAK-3) GPT-2 Janus kinase 3 (JAK-3) mediates the conversion of glucose to glycogen in skeletal muscle. The
increase in glucose uptake by skeletal muscle is believed to be associated with a reduction in muscle protein breakdown and with the release of ketone bodies, which contribute to the metabolism of glucose.
BioGPT Janus kinase 3 (JAK-3) is a member of the Janus kinase (JAK) family of non-receptor tyrosine kinases and plays an important role in the regulation of cell proliferation, differentiation, survival, migration and angiogenesis.
Apricitabine GPT-2 Apricitabine, duloxetine and citalopram) were investigated in patients with a history of
treatment-resistant depression (n = 10). We found that treatment with the three drugs significantly decreased depressive symptoms, but not depressive symptoms over the next 12 weeks. We conclude that antidepressant drugs that reduce depressive symptoms and reduce depressive symptoms over time may be beneficial in patients with recurrent depression.
BioGPT Apricitabine is an oral prodrug of 5-aza-2 ’-deoxycytidine (5-aza-CdR), a DNA methyltransferase (DNMT) inhibitor, which has been approved by the US Food and Drug Administration (FDA) for the treatment of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in combination with low-dose cytarabine (Ara-C) and granulocyte colony-stimulating factor (G-CSF) for patients with intermediate-2 or high-risk MDS or AML.
Xylazine GPT-2 Xylazine, the ‘active ingredient’ in ‘bath salts’ (see above) has been linked to numerous deaths,
including those of former University of California, Berkeley, professor Andrew Anglin, who posted
a video of himself having a bath filled with it.
BioGPT Xylazine is an alpha 2-adrenoceptor agonist which has been used as a sedative and analgesic in veterinary medicine for many years, but its effects on the cardiovascular system have not been extensively studied in the dog, and its effects on the central nervous system (CNS) have not been well characterized in the dog, despite the fact that xylazine has been widely used as a sedative and analgesic in veterinary medicine for more than 30 years.
Psoralen GPT-2 Psoralen, the city of the spiritualist, and had been given to him by God, who wished to show him
the way of salvation, and to draw him back to the true life of Christ, and to bring him into communion with his fellow-man.
BioGPT Psoralen photochemotherapy (PUVA) is a well-established treatment for psoriasis, but its use is limited by the risk of skin cancer, particularly squamous cell carcinoma (SCC) of the head and neck (H & N), which is the most common site of skin cancer in the United States (US)
CP-673451 GPT-2 CP-673451) and 4-5-6-3-1-2-3-4-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27-
BioGPT CP-673451 is a potent, selective, and orally active inhibitor of human neutrophil elastase (HNE) and human cathepsin G (CatG) with in vitro and in vivo anti-inflammatory activity in a variety of animal models of inflammation and in a model of acute lung injury (ALI) in the rat induced by intratracheal instillation of lipopolysaccharide (LPS) and tumor necrosis factor-alpha (TNF-alpha), a model of acute lung injury (ALI) in which neutrophils play an important role.
BIIB-021 GPT-2 BIIB-021A-2-1.pdf. Wright, C. (2002). ‘The War on Terror: A Legal Theory of Terrorist Attacks,’ in
L.E.R. Wright, S.R. Warrick and J.H. Rothman (Eds.), The War on Terror: Essays in Honor of Michael T. Klare (New York: Oxford University Press), 143-57.
BioGPT BIIB-021 is a novel, orally active, non-peptide bradykinin B2 receptor antagonist with potent and long-lasting anti-inflammatory activity in animal models of acute and chronic inflammation and in a rat model of adjuvant-induced arthritis (AIA), an animal model of rheumatoid arthritis (RA) and in a rat model of collagen-induced arthritis (CIA), an animal model of collagen-induced arthritis (CIA), in which arthritis is induced by immunization with bovine type II collagen (CII).
Huang L, Lin J, Li X, Song L, Zheng Z, Wong KC.Brief Bioinform. 2022 Jan 17;23(1):bbab451. doi: 10.1093/bib/bbab451.PMID: 34791012
The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules.
Results: We evaluated the classification part on ‘DDIs 2013’ dataset and ‘DTIs’ dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships.
Jin Q, Yang Y, Chen Q, Lu Z.ArXiv. 2023 May 16:arXiv:2304.09667v3. Preprint.PMID: 37131884 Free PMC article.
While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; (2) GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; (3) Different types of errors are enriched in different tasks, providing valuable insights for future improvements.
PLEASE WATCH THE FOLLOWING VIDEOS ON BIOGPT
This one entitled
Microsoft’s BioGPT Shows Promise as the Best Biomedical NLP
gives a good general description of this new MIT/Microsoft project and its usefullness in scanning 15 million articles on PubMed while returning ChatGPT like answers.
Please note one of the comments which is VERY IMPORTANT
bioGPT is difficult for non-developers to use, and Microsoft researchers seem to default that all users are proficient in Python and ML.
Much like Microsoft Azure it seems this BioGPT is meant for developers who have advanced programming skill. Seems odd then to be paying programmers multiK salaries when one or two Key Opinion Leaders from the medical field might suffice but I would be sure Microsoft will figure this out.
ALSO VIEW VIDEO
This is a talk from Microsoft on BioGPT
Other Relevant Articles on Natural Language Processing in BioInformatics, Healthcare and ChatGPT for Medicine on this Open Access Scientific Journal Include
Mimicking vaginal cells and microbiome interactions on chip microfluidic culture
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Scientists at Harvard University’s Wyss Institute for Biologically Inspired Engineering have developed the world’s first “vagina-on-a-chip,” which uses living cells and bacteria to mimic the microbial environment of the human vagina. It could help to test drugs against bacterial vaginosis, a common microbial imbalance that makes millions of people more susceptible to sexually transmitted diseases and puts them at risk of preterm delivery when pregnant. Vaginal health is difficult to study in a laboratory setting partly because laboratory animals have “totally different microbiomes” than humans. To address this, scientists have created an unique chip, which is an inch-long, rectangular polymer case containing live human vaginal tissue from a donor and a flow of estrogen-carrying material to simulate vaginal mucus.
The organs-on-a-chip mimic real bodily function, making it easier to study diseases and test drugs. Previous examples include models of the lungs and the intestines. In this case, the tissue acts like that of a real vagina in some important ways. It even responds to changes in estrogen by adjusting the expression of certain genes. And it can grow a humanlike microbiome dominated by “good” or “bad” bacteria. The researchers have demonstrated that Lactobacilli growing on the chip’s tissue help to maintain a low pH by producing lactic acid. Conversely, if the researchers introduce Gardnerella, the chip develops a higher pH, cell damage and increased inflammation: classic bacterial vaginosis signs. So, the chip can demonstrate how a healthy / unhealthy microbiome affects the vagina.
The next step is personalization or subject specific culture from individuals. The chip is a real leap forward, it has the prospect of testing how typical antibiotic treatments against bacterial vaginosis affect the different bacterial strains. Critics of organ-on-a-chip technology often raise the point that it models organs in isolation from the rest of the body. There are limitations such as many researchers are interested in vaginal microbiome changes that occur during pregnancy because of the link between bacterial vaginosis and labor complications. Although the chip’s tissue responds to estrogen, but it does not fully mimic pregnancy without feedback loops from other organs. The researchers are already working on connecting the vagina chip to a cervix chip, which could better represent the larger reproductive system.
All these information indicate that the human vagina chip offers a new model to study host-vaginal microbiome interactions in both optimal and non-optimal states, as well as providing a human relevant preclinical model for development and testing of reproductive therapeutics, including live bio-therapeutics products for bacterial vaginosis. This microfluidic human vagina chip that enables flow through an open epithelial lumen also offers a unique advantage for studies on the effect of cervicovaginal mucus on vaginal health as clinical mucus samples or commercially available mucins can be flowed through this channel. The role of resident and circulating immune cells in host-microbiome interactions also can be explored by incorporating these cells into the vagina chip in the future, as this has been successfully done in various other organ chip models.
The following paper in Cells describes the discovery of protein interactors of endoglin, which is recruited to membranes at the TGF-β receptor complex upon TGF-β signaling. Interesting a carbohydrate binding protein, galectin-3, and an E3-ligase, TRIM21, were found to be unique interactors within this complex.
Gallardo-Vara E, Ruiz-Llorente L, Casado-Vela J, Ruiz-Rodríguez MJ, López-Andrés N, Pattnaik AK, Quintanilla M, Bernabeu C. Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners. Cells. 2019 Sep 13;8(9):1082. doi: 10.3390/cells8091082. PMID: 31540324; PMCID: PMC6769930.
Abstract
Endoglin is a 180-kDa glycoprotein receptor primarily expressed by the vascular endothelium and involved in cardiovascular disease and cancer. Heterozygous mutations in the endoglin gene (ENG) cause hereditary hemorrhagic telangiectasia type 1, a vascular disease that presents with nasal and gastrointestinal bleeding, skin and mucosa telangiectases, and arteriovenous malformations in internal organs. A circulating form of endoglin (alias soluble endoglin, sEng), proteolytically released from the membrane-bound protein, has been observed in several inflammation-related pathological conditions and appears to contribute to endothelial dysfunction and cancer development through unknown mechanisms. Membrane-bound endoglin is an auxiliary component of the TGF-β receptor complex and the extracellular region of endoglin has been shown to interact with types I and II TGF-β receptors, as well as with BMP9 and BMP10 ligands, both members of the TGF-β family. To search for novel protein interactors, we screened a microarray containing over 9000 unique human proteins using recombinant sEng as bait. We find that sEng binds with high affinity, at least, to 22 new proteins. Among these, we validated the interaction of endoglin with galectin-3, a secreted member of the lectin family with capacity to bind membrane glycoproteins, and with tripartite motif-containing protein 21 (TRIM21), an E3 ubiquitin-protein ligase. Using human endothelial cells and Chinese hamster ovary cells, we showed that endoglin co-immunoprecipitates and co-localizes with galectin-3 or TRIM21. These results open new research avenues on endoglin function and regulation.
Endoglin is an auxiliary TGF-β co-receptor predominantly expressed in endothelial cells, which is involved in vascular development, repair, homeostasis, and disease [1,2,3,4]. Heterozygous mutations in the human ENDOGLIN gene (ENG) cause hereditary hemorrhagic telangiectasia (HHT) type 1, a vascular disease associated with nasal and gastrointestinal bleeds, telangiectases on skin and mucosa and arteriovenous malformations in the lung, liver, and brain [4,5,6]. The key role of endoglin in the vasculature is also illustrated by the fact that endoglin-KO mice die in utero due to defects in the vascular system [7]. Endoglin expression is markedly upregulated in proliferating endothelial cells involved in active angiogenesis, including the solid tumor neovasculature [8,9]. For this reason, endoglin has become a promising target for the antiangiogenic treatment of cancer [10,11,12]. Endoglin is also expressed in cancer cells where it can behave as both a tumor suppressor in prostate, breast, esophageal, and skin carcinomas [13,14,15,16] and a promoter of malignancy in melanoma and Ewing’s sarcoma [17]. Ectodomain shedding of membrane-bound endoglin may lead to a circulating form of the protein, also known as soluble endoglin (sEng) [18,19,20]. Increased levels of sEng have been found in several vascular-related pathologies, including preeclampsia, a disease of high prevalence in pregnant women which, if left untreated, can lead to serious and even fatal complications for both mother and baby [2,18,19,21]. Interestingly, several lines of evidence support a pathogenic role of sEng in the vascular system, including endothelial dysfunction, antiangiogenic activity, increased vascular permeability, inflammation-associated leukocyte adhesion and transmigration, and hypertension [18,22,23,24,25,26,27]. Because of its key role in vascular pathology, a large number of studies have addressed the structure and function of endoglin at the molecular level, in order to better understand its mechanism of action.
Galectin-3 Interacts with Endoglin in Cells
Galectin-3 is a secreted member of the lectin family with the capacity to bind membrane glycoproteins like endoglin and is involved in the pathogenesis of many human diseases [52]. We confirmed the protein screen data for galectin-3, as evidenced by two-way co-immunoprecipitation of endoglin and galectin-3 upon co-transfection in CHO-K1 cells. As shown in Figure 1A, galectin-3 and endoglin were efficiently transfected, as demonstrated by Western blot analysis in total cell extracts. No background levels of endoglin were observed in control cells transfected with the empty vector (Ø). By contrast, galectin-3 could be detected in all samples but, as expected, showed an increased signal in cells transfected with the galectin-3 expression vector. Co-immunoprecipitation studies of these cell lysates showed that galectin-3 was present in endoglin immunoprecipitates (Figure 1B). Conversely, endoglin was also detected in galectin-3 immunoprecipitates (Figure 1C).
Figure 1. Protein–protein association between galectin-3 and endoglin. (A–C). Co-immunoprecipitation of galectin-3 and endoglin. CHO-K1 cells were transiently transfected with pcEXV-Ø (Ø), pcEXV–HA–EngFL (Eng) and pcDNA3.1–Gal-3 (Gal3) expression vectors. (A) Total cell lysates (TCL) were analyzed by SDS-PAGE under reducing conditions, followed by Western blot (WB) analysis using specific antibodies to endoglin, galectin-3 and β-actin (loading control). Cell lysates were subjected to immunoprecipitation (IP) with anti-endoglin (B) or anti-galectin-3 (C) antibodies, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin or anti-galectin-3 antibodies, as indicated. Negative controls with an IgG2b (B) and IgG1 (C) were included. (D) Protein-protein interactions between galectin-3 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 7.3 µM recombinant human galectin-3/6xHis at the C-terminus (LGALS3), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
Figure 2.Galectin-3 and endoglin co-localize in human endothelial cells. Human umbilical vein-derived endothelial cell (HUVEC) monolayers were fixed with paraformaldehyde, permeabilized with Triton X-100, incubated with the mouse mAb P4A4 anti-endoglin, washed, and incubated with a rabbit polyclonal anti-galectin-3 antibody (PA5-34819). Galectin-3 and endoglin were detected by immunofluorescence upon incubation with Alexa 647 goat anti-rabbit IgG (red staining) and Alexa 488 goat anti-mouse IgG (green staining) secondary antibodies, respectively. (A) Single staining of galectin-3 (red) and endoglin (green) at the indicated magnifications. (B) Merge images plus DAPI (nuclear staining in blue) show co-localization of galectin-3 and endoglin (yellow color). Representative images of five different experiments are shown.
Endoglin associates with the cullin-type E3 ligase TRIM21
Figure 3.Protein–protein association between TRIM21 and endoglin. (A–E) Co-immunoprecipitation of TRIM21 and endoglin. A,B. HUVEC monolayers were lysed and total cell lysates (TCL) were subjected to SDS-PAGE under reducing (for TRIM21 detection) or nonreducing (for endoglin detection) conditions, followed by Western blot (WB) analysis using antibodies to endoglin, TRIM21 or β-actin (A). HUVECs lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or negative control antibodies, followed by WB analysis with anti-endoglin (B). C,D. CHO-K1 cells were transiently transfected with pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (E) or pcDNA3.1–HA–hTRIM21 (T) expression vectors, as indicated. Total cell lysates (TCL) were subjected to SDS-PAGE under nonreducing conditions and WB analysis using specific antibodies to endoglin, TRIM21, and β-actin (C). Cell lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or anti-endoglin antibodies, followed by SDS-PAGE under reducing (upper panel) or nonreducing (lower panel) conditions and WB analysis with anti-TRIM21 or anti-endoglin antibodies. Negative controls of appropriate IgG were included (D). E. CHO-K1 cells were transiently transfected with pcDNA3.1–HA–hTRIM21 and pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (FL; full-length), pDisplay–HA–EngEC (EC; cytoplasmic-less) or pDisplay–HA–EngTMEC (TMEC; cytoplasmic-less) expression vectors, as indicated. Cell lysates were subjected to immunoprecipitation with anti-TRIM21, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin antibodies, as indicated. The asterisk indicates the presence of a nonspecific band. Mr, molecular reference; Eng, endoglin; TRIM, TRIM21. (F) Protein–protein interactions between TRIM21 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 5.4 µM recombinant human TRIM21/6xHis at the N-terminus (R052), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
Table 1. Human protein-array analysis of endoglin interactors1.
1 Microarrays containing over 9000 unique human proteins were screened using recombinant sEng as a probe. Protein interactors showing the highest scores (Z-score ≥2.0) are listed. GeneBank (https://www.ncbi.nlm.nih.gov/genbank/) and UniProtKB (https://www.uniprot.org/help/uniprotkb) accession numbers are indicated with a yellow or green background, respectively. The cellular compartment of each protein was obtained from the UniProtKB webpage. Proteins selected for further studies (TRIM21 and galectin-3) are indicated in bold type with blue background.
Note: the following are from NCBI Genbank and Genecards on TRIM21
Official Symbol TRIM21provided by HGNC Official Full Name tripartite motif containing 21provided by HGNC Primary source HGNC:HGNC:11312 See related Ensembl:ENSG00000132109MIM:109092;AllianceGenome:HGNC:11312 Gene type protein coding RefSeq status REVIEWED Organism Homo sapiens Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo Also known as SSA; RO52; SSA1; RNF81; Ro/SSA Summary This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008] Expression Ubiquitous expression in spleen (RPKM 15.5), appendix (RPKM 13.2) and 24 other tissues See more Orthologs mouseall NEW Try the new Gene table Try the new Transcript table
This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]
E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression. Enhances the decapping activity of DCP2. Exists as a ribonucleoprotein particle present in all mammalian cells studied and composed of a single polypeptide and one of four small RNA molecules. At least two isoforms are present in nucleated and red blood cells, and tissue specific differences in RO/SSA proteins have been identified. The common feature of these proteins is their ability to bind HY RNAs.2. Involved in the regulation of innate immunity and the inflammatory response in response to IFNG/IFN-gamma. Organizes autophagic machinery by serving as a platform for the assembly of ULK1, Beclin 1/BECN1 and ATG8 family members and recognizes specific autophagy targets, thus coordinating target recognition with assembly of the autophagic apparatus and initiation of autophagy. Acts as an autophagy receptor for the degradation of IRF3, hence attenuating type I interferon (IFN)-dependent immune responses (PubMed:26347139, 16297862, 16316627, 16472766, 16880511, 18022694, 18361920, 18641315, 18845142, 19675099). Represses the innate antiviral response by facilitating the formation of the NMI-IFI35 complex through ‘Lys-63’-linked ubiquitination of NMI (PubMed:26342464). ( RO52_HUMAN,P19474 )
Molecular function for TRIM21 Gene according to UniProtKB/Swiss-Prot
Function:
E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression.
Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners
Gallardo-Vara E, Ruiz-Llorente L, Casado-Vela J, Ruiz-Rodríguez MJ, López-Andrés N, Pattnaik AK, Quintanilla M, Bernabeu C. Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners. Cells. 2019 Sep 13;8(9):1082. doi: 10.3390/cells8091082. PMID: 31540324; PMCID: PMC6769930.
Abstract
Endoglin is a 180-kDa glycoprotein receptor primarily expressed by the vascular endothelium and involved in cardiovascular disease and cancer. Heterozygous mutations in the endoglin gene (ENG) cause hereditary hemorrhagic telangiectasia type 1, a vascular disease that presents with nasal and gastrointestinal bleeding, skin and mucosa telangiectases, and arteriovenous malformations in internal organs. A circulating form of endoglin (alias soluble endoglin, sEng), proteolytically released from the membrane-bound protein, has been observed in several inflammation-related pathological conditions and appears to contribute to endothelial dysfunction and cancer development through unknown mechanisms. Membrane-bound endoglin is an auxiliary component of the TGF-β receptor complex and the extracellular region of endoglin has been shown to interact with types I and II TGF-β receptors, as well as with BMP9 and BMP10 ligands, both members of the TGF-β family. To search for novel protein interactors, we screened a microarray containing over 9000 unique human proteins using recombinant sEng as bait. We find that sEng binds with high affinity, at least, to 22 new proteins. Among these, we validated the interaction of endoglin with galectin-3, a secreted member of the lectin family with capacity to bind membrane glycoproteins, and with tripartite motif-containing protein 21 (TRIM21), an E3 ubiquitin-protein ligase. Using human endothelial cells and Chinese hamster ovary cells, we showed that endoglin co-immunoprecipitates and co-localizes with galectin-3 or TRIM21. These results open new research avenues on endoglin function and regulation.
Endoglin is an auxiliary TGF-β co-receptor predominantly expressed in endothelial cells, which is involved in vascular development, repair, homeostasis, and disease [1,2,3,4]. Heterozygous mutations in the human ENDOGLIN gene (ENG) cause hereditary hemorrhagic telangiectasia (HHT) type 1, a vascular disease associated with nasal and gastrointestinal bleeds, telangiectases on skin and mucosa and arteriovenous malformations in the lung, liver, and brain [4,5,6]. The key role of endoglin in the vasculature is also illustrated by the fact that endoglin-KO mice die in utero due to defects in the vascular system [7]. Endoglin expression is markedly upregulated in proliferating endothelial cells involved in active angiogenesis, including the solid tumor neovasculature [8,9]. For this reason, endoglin has become a promising target for the antiangiogenic treatment of cancer [10,11,12]. Endoglin is also expressed in cancer cells where it can behave as both a tumor suppressor in prostate, breast, esophageal, and skin carcinomas [13,14,15,16] and a promoter of malignancy in melanoma and Ewing’s sarcoma [17]. Ectodomain shedding of membrane-bound endoglin may lead to a circulating form of the protein, also known as soluble endoglin (sEng) [18,19,20]. Increased levels of sEng have been found in several vascular-related pathologies, including preeclampsia, a disease of high prevalence in pregnant women which, if left untreated, can lead to serious and even fatal complications for both mother and baby [2,18,19,21]. Interestingly, several lines of evidence support a pathogenic role of sEng in the vascular system, including endothelial dysfunction, antiangiogenic activity, increased vascular permeability, inflammation-associated leukocyte adhesion and transmigration, and hypertension [18,22,23,24,25,26,27]. Because of its key role in vascular pathology, a large number of studies have addressed the structure and function of endoglin at the molecular level, in order to better understand its mechanism of action.
Galectin-3 Interacts with Endoglin in Cells
Galectin-3 is a secreted member of the lectin family with the capacity to bind membrane glycoproteins like endoglin and is involved in the pathogenesis of many human diseases [52]. We confirmed the protein screen data for galectin-3, as evidenced by two-way co-immunoprecipitation of endoglin and galectin-3 upon co-transfection in CHO-K1 cells. As shown in Figure 1A, galectin-3 and endoglin were efficiently transfected, as demonstrated by Western blot analysis in total cell extracts. No background levels of endoglin were observed in control cells transfected with the empty vector (Ø). By contrast, galectin-3 could be detected in all samples but, as expected, showed an increased signal in cells transfected with the galectin-3 expression vector. Co-immunoprecipitation studies of these cell lysates showed that galectin-3 was present in endoglin immunoprecipitates (Figure 1B). Conversely, endoglin was also detected in galectin-3 immunoprecipitates (Figure 1C).
Figure 1. Protein–protein association between galectin-3 and endoglin. (A–C). Co-immunoprecipitation of galectin-3 and endoglin. CHO-K1 cells were transiently transfected with pcEXV-Ø (Ø), pcEXV–HA–EngFL (Eng) and pcDNA3.1–Gal-3 (Gal3) expression vectors. (A) Total cell lysates (TCL) were analyzed by SDS-PAGE under reducing conditions, followed by Western blot (WB) analysis using specific antibodies to endoglin, galectin-3 and β-actin (loading control). Cell lysates were subjected to immunoprecipitation (IP) with anti-endoglin (B) or anti-galectin-3 (C) antibodies, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin or anti-galectin-3 antibodies, as indicated. Negative controls with an IgG2b (B) and IgG1 (C) were included. (D) Protein-protein interactions between galectin-3 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 7.3 µM recombinant human galectin-3/6xHis at the C-terminus (LGALS3), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
Figure 2.Galectin-3 and endoglin co-localize in human endothelial cells. Human umbilical vein-derived endothelial cell (HUVEC) monolayers were fixed with paraformaldehyde, permeabilized with Triton X-100, incubated with the mouse mAb P4A4 anti-endoglin, washed, and incubated with a rabbit polyclonal anti-galectin-3 antibody (PA5-34819). Galectin-3 and endoglin were detected by immunofluorescence upon incubation with Alexa 647 goat anti-rabbit IgG (red staining) and Alexa 488 goat anti-mouse IgG (green staining) secondary antibodies, respectively. (A) Single staining of galectin-3 (red) and endoglin (green) at the indicated magnifications. (B) Merge images plus DAPI (nuclear staining in blue) show co-localization of galectin-3 and endoglin (yellow color). Representative images of five different experiments are shown.
Endoglin associates with the cullin-type E3 ligase TRIM21
Figure 3.Protein–protein association between TRIM21 and endoglin. (A–E) Co-immunoprecipitation of TRIM21 and endoglin. A,B. HUVEC monolayers were lysed and total cell lysates (TCL) were subjected to SDS-PAGE under reducing (for TRIM21 detection) or nonreducing (for endoglin detection) conditions, followed by Western blot (WB) analysis using antibodies to endoglin, TRIM21 or β-actin (A). HUVECs lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or negative control antibodies, followed by WB analysis with anti-endoglin (B). C,D. CHO-K1 cells were transiently transfected with pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (E) or pcDNA3.1–HA–hTRIM21 (T) expression vectors, as indicated. Total cell lysates (TCL) were subjected to SDS-PAGE under nonreducing conditions and WB analysis using specific antibodies to endoglin, TRIM21, and β-actin (C). Cell lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or anti-endoglin antibodies, followed by SDS-PAGE under reducing (upper panel) or nonreducing (lower panel) conditions and WB analysis with anti-TRIM21 or anti-endoglin antibodies. Negative controls of appropriate IgG were included (D). E. CHO-K1 cells were transiently transfected with pcDNA3.1–HA–hTRIM21 and pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (FL; full-length), pDisplay–HA–EngEC (EC; cytoplasmic-less) or pDisplay–HA–EngTMEC (TMEC; cytoplasmic-less) expression vectors, as indicated. Cell lysates were subjected to immunoprecipitation with anti-TRIM21, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin antibodies, as indicated. The asterisk indicates the presence of a nonspecific band. Mr, molecular reference; Eng, endoglin; TRIM, TRIM21. (F) Protein–protein interactions between TRIM21 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 5.4 µM recombinant human TRIM21/6xHis at the N-terminus (R052), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
Table 1. Human protein-array analysis of endoglin interactors1.
1 Microarrays containing over 9000 unique human proteins were screened using recombinant sEng as a probe. Protein interactors showing the highest scores (Z-score ≥2.0) are listed. GeneBank (https://www.ncbi.nlm.nih.gov/genbank/) and UniProtKB (https://www.uniprot.org/help/uniprotkb) accession numbers are indicated with a yellow or green background, respectively. The cellular compartment of each protein was obtained from the UniProtKB webpage. Proteins selected for further studies (TRIM21 and galectin-3) are indicated in bold type with blue background.
Note: the following are from NCBI Genbank and Genecards on TRIM21
This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]
Expression
Ubiquitous expression in spleen (RPKM 15.5), appendix (RPKM 13.2) and 24 other tissues See more
This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]
E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression. Enhances the decapping activity of DCP2. Exists as a ribonucleoprotein particle present in all mammalian cells studied and composed of a single polypeptide and one of four small RNA molecules. At least two isoforms are present in nucleated and red blood cells, and tissue specific differences in RO/SSA proteins have been identified. The common feature of these proteins is their ability to bind HY RNAs.2. Involved in the regulation of innate immunity and the inflammatory response in response to IFNG/IFN-gamma. Organizes autophagic machinery by serving as a platform for the assembly of ULK1, Beclin 1/BECN1 and ATG8 family members and recognizes specific autophagy targets, thus coordinating target recognition with assembly of the autophagic apparatus and initiation of autophagy. Acts as an autophagy receptor for the degradation of IRF3, hence attenuating type I interferon (IFN)-dependent immune responses (PubMed:26347139, 16297862, 16316627, 16472766, 16880511, 18022694, 18361920, 18641315, 18845142, 19675099). Represses the innate antiviral response by facilitating the formation of the NMI-IFI35 complex through ‘Lys-63’-linked ubiquitination of NMI (PubMed:26342464). ( RO52_HUMAN,P19474 )
Molecular function for TRIM21 Gene according to UniProtKB/Swiss-Prot
Function:
E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression.
Other Articles in this Open Access Scientific Journal on Galectins and Proteosome Include
Accelerating PROTAC drug discovery: Establishing a relationship between ubiquitination and target protein degradation
Curator: Stephen J. Williams, Ph.D.
PROTACs have been explored in multiple disease fields with focus on only few ligases like cereblon (CRBN), Von Hippel-Lindau (VHL), IAP and MDM2. Cancer targets like androgen receptor, estrogen receptor, BTK, BCL2, CDK8 and c-MET [[6], [7], [8], [9], [10], [11]] have been successfully targeted using PROTACs. A variety of BET family (BRD2, BRD3, and BRD4)- PROTACs were designed using multiple ligases; MDM2-based BRD4 PROTAC [12], CRBN based dBET1 [13] and BETd-24-6 [14] for triple-negative breast cancer, enhanced membrane permeable dBET6 [15], and dBET57 PROTAC [16]. PROTACs for Hepatitis c virus (HCV) protease, IRAK4 and Tau [[17], [18], [19]] have been explored for viral, immune and neurodegenerative diseases, respectively. Currently, the PROTAC field expansion to vast undruggable proteome is hindered due to narrow focus on select E3 ligases. Lack of reliable tools to rapidly evaluate PROTACs based on new ligases is hindering the progress. Screening platforms designed must be physiologically relevant and represent true PROTAC cellular function, i.e., PROTAC-mediated target ubiquitination and degradation.
In the current study, we employ TUBEs as affinity capture reagents to monitor PROTAC-induced poly-ubiquitination and degradation as a measure of potency. We established and validated proof-of-concept cell-based assays in a 96-well format using PROTACS for three therapeutic targets BET family proteins, kinases, and KRAS. To our knowledge, the proposed PROTAC assays are first of its kind that can simultaneously 1) detect ubiquitination of endogenous, native protein targets, 2) evaluate the potency of PROTACs, and 3) establish a link between the UPS and protein degradation. Using these TUBE assays, we established rank order potencies between four BET family PROTACs dBET1, dBET6, BETd246 and dBET57 based on peak ubiquitination signals (“UbMax”) of the target protein. TUBE assay was successful in demonstrating promiscuous kinase PROTACs efficiency to degrade Aurora Kinase A at sub-nanomolar concentrations within 1 h. A comparative study to identify changes in the ubiquitination and degradation profile of KRAS G12C PROTACs recruiting two E3 ligases (CRBN and VHL). All of the ubiquitination and degradation profiles obtained from TUBE based assays correlate well with traditional low throughput immunoblotting. Significant correlation between DC50 obtained from protein degradation in western blotting and UbMax values demonstrates our proposed assays can aid in high-throughput screening and drastically eliminate artifacts to overcome bottlenecks in PROTAC drug discovery.
To successfully set up HTS screening with novel PROTACs without pre-existing knowledge, we recommend the following steps. 1. Identify a model PROTAC that can potentially demonstrate activity based on knowledge in PROTAC design or in vitro binding studies. 2. Perform a time course study with 2–3 doses of the model PROTAC based on affinities of the ligands selected. 3. Monitor ubiquitination and degradation profiles using plate-based assay and identify time point that demonstrates UbMax. 4. Perform a dose response at selected time point with a library of PROTACs to establish rank order potency.
INTRODUCTION
Ubiquitination is a major regulatory mechanism to maintain cellular protein homeostasis by marking proteins for proteasomal-mediated degradation [1]. Given ubiquitin’s role in a variety of pathologies, the idea of targeting the Ubiquitin Proteasome System (UPS) is at the forefront of drug discovery [2]. “Event-driven” protein degradation using the cell’s own UPS is a promising technology for addressing the “undruggable” proteome [3]. Targeted protein degradation (TPD) has emerged as a new paradigm and promising therapeutic option to selectively attack previously intractable drug targets using PROteolytic TArgeting Chimeras (PROTACs) [4]. PROTACs are heterobifunctional molecules with a distinct ligand that targets a specific E3 ligase which is tethered to another ligand specific for the target protein using an optimized chemical linker. A functional PROTAC induces a ternary E3-PROTAC-target complex, resulting in poly-ubiquitination and subsequent controlled protein degradation [5]. Ability to function at sub-stoichiometric levels for efficient degradation, a significant advantage over traditional small molecules.
PROTACs have been explored in multiple disease fields with focus on only few ligases like cereblon (CRBN), Von Hippel-Lindau (VHL), IAP and MDM2. Cancer targets like androgen receptor, estrogen receptor, BTK, BCL2, CDK8 and c-MET [[6], [7], [8], [9], [10], [11]] have been successfully targeted using PROTACs. A variety of BET family (BRD2, BRD3, and BRD4)- PROTACs were designed using multiple ligases; MDM2-based BRD4 PROTAC [12], CRBN based dBET1 [13] and BETd-24-6 [14] for triple-negative breast cancer, enhanced membrane permeable dBET6 [15], and dBET57 PROTAC [16]. PROTACs for Hepatitis c virus (HCV) protease, IRAK4 and Tau [[17], [18], [19]] have been explored for viral, immune and neurodegenerative diseases, respectively. Currently, the PROTAC field expansion to vast undruggable proteome is hindered due to narrow focus on select E3 ligases. Lack of reliable tools to rapidly evaluate PROTACs based on new ligases is hindering the progress. Screening platforms designed must be physiologically relevant and represent true PROTAC cellular function, i.e., PROTAC-mediated target ubiquitination and degradation.
Cellular PROTAC screening is traditionally performed using cell lines harboring reporter genes and/or Western blotting. While Western blotting is easy to perform, they are low throughput, semi-quantitative and lack sensitivity. While reporter gene assays address some of the issues, they are challenged by reporter tags having internal lysines leading to artifacts. Currently, no approaches are available that can identify true PROTAC effects such as target ubiquitination and proteasome-mediated degradation simultaneously. High affinity ubiquitin capture reagents like TUBEs [20] (tandem ubiquitin binding entities), are engineered ubiquitin binding domains (UBDs) that allow for detection of ultralow levels of polyubiquitinated proteins under native conditions with affinities as low as 1 nM. The versatility and selectivity of TUBEs makes them superior to antibodies, and they also offer chain-selectivity (-K48, -K63, or linear) [21]. High throughput assays that can report the efficacy of multiple PROTACs simultaneously by monitoring PROTAC mediated ubiquitination can help establish rank order potency and guide chemists in developing meaningful structure activity relationships (SAR) rapidly.
In the current study, we employ TUBEs as affinity capture reagents to monitor PROTAC-induced poly-ubiquitination and degradation as a measure of potency. We established and validated proof-of-concept cell-based assays in a 96-well format using PROTACS for three therapeutic targets BET family proteins, kinases, and KRAS. To our knowledge, the proposed PROTAC assays are first of its kind that can simultaneously 1) detect ubiquitination of endogenous, native protein targets, 2) evaluate the potency of PROTACs, and 3) establish a link between the UPS and protein degradation. Using these TUBE assays, we established rank order potencies between four BET family PROTACs dBET1, dBET6, BETd246 and dBET57 based on peak ubiquitination signals (“UbMax”) of the target protein. TUBE assay was successful in demonstrating promiscuous kinase PROTACs efficiency to degrade Aurora Kinase A at sub-nanomolar concentrations within 1 h. A comparative study to identify changes in the ubiquitination and degradation profile of KRAS G12C PROTACs recruiting two E3 ligases (CRBN and VHL). All of the ubiquitination and degradation profiles obtained from TUBE based assays correlate well with traditional low throughput immunoblotting. Significant correlation between DC50 obtained from protein degradation in western blotting and UbMax values demonstrates our proposed assays can aid in high-throughput screening and drastically eliminate artifacts to overcome bottlenecks in PROTAC drug discovery.
Fig. 1. Schematic representation of TUBE assay to monitor PROTAC mediated cellular ubiquitination of target proteins.
Fig. 2. TUBE based assay screening of PROTACs: Jurkat cell lysates were treated with BRD3-specific PROTACs A) dBET1, B) dBET6, C) BETd24-6, and D) dBET57. Polyubiquitination profiles and Ubmax of BRD3 for each PROTAC were represented as relative CL intensity. Relative CL intensities were calculated by dividing raw CL signals from a given PROTAC dose over DMSO treated samples. Error bars represent standard deviations, n = 3.
Fig. 3. PROTAC mediated degradation of bromodomain proteins analyzed by anti-BRD3 western blotting. Dose response of PROTACs dBET1, dBET6, Betd-24-6 and dBET57 at 45 min in Jurkat cells demonstrates degradation of BRD3, Acting as loading control.
Fig. 4. PROTAC mediated ubiquitination and degradation of AURKA in K562 cells. (A) Time course study to evaluate intracellular ubiquitination and degradation. (B) Western blot analysis of time course study: degradation kinetics (C) A dose response study to evaluate DC50 of the promiscuous kinase PROTAC in K562 cells. (D) Western blot analysis of dose response study to monitor degradation, GAPDH as loading control. Error bars represent standard deviation, n = 3.
Sperm damage and fertility problem due to COVID-19
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Many couples initially deferred attempts at pregnancy or delayed fertility care due to concerns about coronavirus disease 2019 (COVID-19). One significant fear during the COVID-19 pandemic was the possibility of sexual transmission. Many couples have since resumed fertility care while accepting the various uncertainties associated with severe acute respiratory syndrome coronavirus 2 (SARS-Cov2), including the evolving knowledge related to male reproductive health. Significant research has been conducted exploring viral shedding, tropism, sexual transmission, the impact of male reproductive hormones, and possible implications to semen quality. However, to date, limited definitive evidence exists regarding many of these aspects, creating a challenging landscape for both patients and physicians to obtain and provide the best clinical care.
According to a new study, which looked at sperm quality in patients who suffered symptomatic coronavirus (COVID-19) infections, showed that it could impact fertility for weeks after recovery from the virus. The data showed 60% COVID-19 infected men had reduction in sperm motility and 37% had drop in sperm count, but, 2 months after recovery from COVID-19 the value came down to 28% and 6% respectively. The researchers also of the view that COVID-19 could not be sexually transmitted through semen after a person had recovered from illness. Patients with mild and severe cases of COVID-19 showed similar rate of drop in sperm quality. But further work is required to establish whether or not COVID-19 could have a longer-term impact on fertility. The estimated recovery time is three months, but further follow-up studies are still required to confirm this and to determine if permanent damage occurred in a minority of men.
Some viruses like influenza are already known to damage sperm mainly by increasing body temperature. But in the case of COVID-19, the researchers found no link between the presence or severity of fever and sperm quality. Tests showed that higher concentrations of specific COVID-19 antibodies in patients’ blood serum were strongly correlated with reduced sperm function. So, it was believed the sperm quality reduction cause could be linked to the body’s immune response to the virus. While the study showed that there was no COVID-19 RNA present in the semen of patients who had got over the virus, the fact that antibodies were attacking sperm suggests the virus may cross the blood-testis barrier during the peak of an infection.
It was found in a previous report that SARS-CoV-2 can be present in the semen of patients with COVID-19, and SARS-CoV-2 may still be detected in the semen of recovering patients. Due to imperfect blood-testes/deferens/epididymis barriers, SARS-CoV-2 might be seeded to the male reproductive tract, especially in the presence of systemic local inflammation. Even if the virus cannot replicate in the male reproductive system, it may persist, possibly resulting from the privileged immunity of testes.
If it could be proved that SARS-CoV-2 can be transmitted sexually in future studies, sexual transmission might be a critical part of the prevention of transmission, especially considering the fact that SARS-CoV-2 was detected in the semen of recovering patients. Abstinence or condom use might be considered as preventive means for these patients. In addition, it is worth noting that there is a need for studies monitoring fetal development. Therefore, to avoid contact with the patient’s saliva and blood may not be enough, since the survival of SARS-CoV-2 in a recovering patient’s semen maintains the likelihood to infect others. But further studies are required with respect to the detailed information about virus shedding, survival time, and concentration in semen.
Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. Although, age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically.
Researchers described a technique combining genomic sequencing with machine-learning methods to predict the possibility a woman will undergo a miscarriage because of egg aneuploidy—a term describing a human egg with an abnormal number of chromosomes. The scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allowed researchers to home in on the protein coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.
As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes—MCM5, FGGY and DDX60L—that when mutated and are highly associated with a risk of producing eggs with aneuploidy. So, the report demonstrated that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways that were identified in the present study are promising targets for future aneuploidy studies. Identifying genetic variations with more predictive power will serve women and their treating clinicians with better information.
New studies link cell cycle proteins to immunosurveillance of premalignant cells
Curator: Stephen J. Williams, Ph.D.
The following is from a Perspectives article in the journal Science by Virinder Reen and Jesus Gil called “Clearing Stressed Cells: Cell cycle arrest produces a p21-dependent secretome that initaites immunosurveillance of premalignant cells”. This is a synopsis of the Sturmlechener et al. research article in the same issue (2).
Complex organisms repair stress-induced damage to limit the replication of faulty cells that could drive cancer. When repair is not possible, tissue homeostasis is maintained by the activation of stress response programs such as apoptosis, which eliminates the cells, or senescence, which arrests them (1). Cellular senescence causes the arrest of damaged cells through the induction of cyclin-dependent kinase inhibitors (CDKIs) such as p16 and p21 (2). Senescent cells also produce a bioactive secretome (the senescence-associated secretory phenotype, SASP) that places cells under immunosurveillance, which is key to avoiding the detrimental inflammatory effects caused by lingering senescent cells on surrounding tissues. On page 577 of this issue, Sturmlechner et al. (3) report that induction of p21 not only contributes to the arrest of senescent cells, but is also an early signal that primes stressed cells for immunosurveillance.Senescence is a complex program that is tightly regulated at the epigenetic and transcriptional levels. For example, exit from the cell cycle is controlled by the induction of p16 and p21, which inhibit phosphorylation of the retinoblastoma protein (RB), a transcriptional regulator and tumor suppressor. Hypophosphorylated RB represses transcription of E2F target genes, which are necessary for cell cycle progression. Conversely, production of the SASP is regulated by a complex program that involves super-enhancer (SE) remodeling and activation of transcriptional regulators such as nuclear factor κB (NF-κB) or CCAAT enhancer binding protein–β (C/EBPβ) (4).
Senescence is a complex program that is tightly regulated at the epigenetic and transcriptional levels. For example, exit from the cell cycle is controlled by the induction of p16 and p21, which inhibit phosphorylation of the retinoblastoma protein (RB), a transcriptional regulator and tumor suppressor. Hypophosphorylated RB represses transcription of E2F target genes, which are necessary for cell cycle progression. Conversely, production of the SASP is regulated by a complex program that involves super-enhancer (SE) remodeling and activation of transcriptional regulators such as nuclear factor κB (NF-κB) or CCAAT enhancer binding protein–β (C/EBPβ) (4).
Sturmlechner et al. found that activation of p21 following stress rapidly halted cell cycle progression and triggered an internal biological timer (of ∼4 days in hepatocytes), allowing time to repair and resolve damage (see the figure). In parallel, C-X-C motif chemokine 14 (CXCL14), a component of the PASP, attracted macrophages to surround and closely surveil these damaged cells. Stressed cells that recovered and normalized p21 expression suspended PASP production and circumvented immunosurveillance. However, if the p21-induced stress was unmanageable, the repair timer expired, and the immune cells transitioned from surveillance to clearance mode. Adjacent macrophages mounted a cytotoxic T lymphocyte response that destroyed damaged cells. Notably, the overexpression of p21 alone was sufficient to orchestrate immune killing of stressed cells, without the need of a senescence phenotype. Overexpression of other CDKIs, such as p16 and p27, did not trigger immunosurveillance, likely because they do not induce CXCL14 expression.In the context of cancer, senescent cell clearance was first observed following reactivation of the tumor suppressor p53 in liver cancer cells. Restoring p53 signaling induced senescence and triggered the elimination of senescent cells by the innate immune system, prompting tumor regression (5). Subsequent work has revealed that the SASP alerts the immune system to target preneoplastic senescent cells. Hepatocytes expressing the oncogenic mutant NRASG12V (Gly12→Val) become senescent and secrete chemokines and cytokines that trigger CD4+ T cell–mediated clearance (6). Despite the relevance for tumor suppression, relatively little is known about how immunosurveillance of oncogene-induced senescent cells is initiated and controlled.
Source of image: Reen, V. and Gil, J. Clearing Stressed Cells. Science Perspectives 2021;Vol 374(6567) p 534-535.
References
2. Sturmlechner I, Zhang C, Sine CC, van Deursen EJ, Jeganathan KB, Hamada N, Grasic J, Friedman D, Stutchman JT, Can I, Hamada M, Lim DY, Lee JH, Ordog T, Laberge RM, Shapiro V, Baker DJ, Li H, van Deursen JM. p21 produces a bioactive secretome that places stressed cells under immunosurveillance. Science. 2021 Oct 29;374(6567):eabb3420. doi: 10.1126/science.abb3420. Epub 2021 Oct 29. PMID: 34709885.
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