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Article SELECTION from Collection of Aviva Lev-Ari, PhD, RN Scientific Articles on PULSE on LinkedIn.com for Training Small Language Models (SLMs) in Domain-aware Content of Medical, Pharmaceutical, Life Sciences and Healthcare by 15 Subjects Matter
Article selection: Aviva Lev-Ari, PhD, RN
#1 – February 20, 2016
Contributions to Personalized and Precision Medicine & Genomic Research
Real Time Conferecence Coverage: Advancing Precision Medicine Conference Philadelphia PA November 1,2 2024 Deliverables
Curator:Stephen J. Williams, Ph.D.
Below are deliverables in form of real Time conference coverage from the Advancing Precision Medicine Confererence held this year in Philadelphia, PA. The meeting brought together scientists and clinicians to discuss the challenges faced in implementing genomics and proteomics into precision medicine decision making workflow. As summarized by a future release at the 2025 ASCO, there are many issues and hindrances to incorporating data obtained from sequencing to make a personalized medicine strategy. The meeting focused on two main disease states: oncology and cardiovascular however most of the live meeting notes are from the oncology tract. In general it was discussed there are three areas which need to be addressed to correctly and more frequently incorporate precision medicine and genomic panel testing into clinical decision making workflow:
access to testing panels and testing methodology for both doctors and patients
expert interpretation of results including algorithms needed to analyze the data
more education of molecular biology and omics data and methodology in medical school to address knowledge gaps between clinicians and scientists
The issues can be summarized by a JCO report to ASCO in 2022:
Personalized medicine presents new opportunities for patients with cancer. However, many patients do not receive the most effective personalized treatments because of challenges associated with integrating predictive biomarker testing into clinical care. Patients are lost at various steps along the precision oncology pathway because of operational inefficiencies, limited understanding of biomarker strategies, inappropriate testing result usage, and access barriers. We examine the impact of various clinical practice gaps associated with diagnostic testing-informed personalized medicine strategies on the treatment of advanced non–small-cell lung cancer (aNSCLC).
The authors used a Diaceutics’ Data Repository, a multisource database including commercial and Medicare claims and laboratory data from over 500,000 patients with non–small-cell lung cancer in the United States. They analyzed the number of patients with newly diagnosed aNSCLC who could have, but did not, benefit from a personalized treatment. The analysis was focused on identifying the gaps and at which steps during care did gaps existed which precipitated either lack of use of precision medicine testing or incorrect interpretation of results.
Their conclusions were alarming:
Most patients with aNSCLC eligible for precision oncology treatments do not benefit from them because of clinical practice gaps. This finding is likely reflective of similar gaps in other cancer types. An increased understanding of the impact of each practice gap can inform strategies to improve the delivery of precision oncology, helping to fully realize the promise of personalized medicine.
The links to the live meeting notes are given below and collection of tweets follow (please note this meeting did not have a Twitter hashtag)
Real Time Coverage Advancing Precision Medicine Annual Conference, Philadelphia PA November 1,2 2024
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
Protein Switches: The Programmable Future of Bio-therapeutics
Curator: Dr. Sudipta Saha, Ph. D.
A PNAS paper entitled “A protein therapeutic modality founded on molecular regulation” presents a pioneering approach to creating protein switches—engineered enzymes that activate only in specific molecular environments. This design introduces a new class of context-dependent therapeutics for precision medicine.
Using domain-insertion techniques, researchers inserted ligand-binding domains into scaffold proteins like β-lactamase. These proteins remain inactive until encountering a specific small molecule, which triggers a conformational change and restores enzymatic activity. This offers precise spatiotemporal control—ideal for minimizing off-target effects.
One key innovation is the systematic insertional mutagenesis that identifies functional switch sites across the protein scaffold. This enables the construction of vast protein libraries, increasing the likelihood of finding optimal switch configurations. Furthermore, the approach is modular—different binding domains and enzymes can be combined to create switches tailored to specific clinical contexts.
These smart proteins can be programmed to respond to cancer biomarkers, metabolite levels, or disease-specific molecular cues. By activating only under disease conditions, they provide a blueprint for next-generation bio-therapeutics—potent, selective, and safer.
The method also opens avenues for drug delivery systems, diagnostics, and biosensors, where conditional activation is critical. Overall, this work represents a conceptual leap in synthetic biology and bioengineering, with implications spanning oncology, infectious disease, and regenerative medicine.
Studies are showing that genetic tests are being ordered at a sufficient rate however it appears there are problems in interpretation and developing treatment plans based on omics testing results
30 % of patients in past and now currently half of all patients are not being given the proper treatment based on genomic testing results (ASCO)
E.g. only 1.5% with NTRK fusions received a NTRK based therapy (this was > 4000 patients receiving wrong therapy)
A lung oncologist may only see one patient with NTRK fusion in three years
Precision Medicine Practice Gaps
48% of oncologist surveyed agreed pathologist needs to be more informed and relevant in the decision making process with regard to tests needing to be ordered
95% said need to flip cost issues ; what does it cost not to get a test … i.e. what is the cost of the wrong therapy
We need a new commercialization model for therapeutic development for this new era of “n of one” patient
There are some tumor markers approved by FDA that cant just be measured by NGS and are correlated with a pathologic complete response
Many point mutations will have no actionable drug
Many alterations are post-genomic meaning there is a post translational component to many prognostic biomarkers
Prevalence of point mutation with no actionable mutation is a limit of NGS
It is important to look at phospho protein spectrum as a potential biomarker
Reverse phase protein proteomic analysis
Made into CLIA based array
They trained centers around the US on the technology and analysis
Basing proteomics or protein markers by traditional IHC requires much antibody validation so if the mass spectrometry field can catch up it would be very powerful
With multiple MRM.MS there is too low abundance of phosphoproteins to allow for good detection
They conducted the I-SPY2 trial for breast cancer and determining if phosphoproteins could be a good biomarker panel
They found they could predict a HER2 response better than NGS
There were patients who were predicted HER2 negative that actually had an activated HER2 signaling pathway by proteomics so NGS must have had a series of false negatives
HER2 co phosphorylation predicts pathologic complete response and predicts therapy by herceptin
They found patients classified as HER2 negative by FISH were HER2 positive by proteomics and had HER2 activation
Nobel Prize in Chemistry 2024 to David Baker, Demis Hassabis and John M. Jumper
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 10/22/2024
ProteinMPNN, which is now available free on the open-source software repository GitHub, will give researchers the tools to make unlimited new designs. “The challenge, of course … is what are you going to design?” Baker says.
In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half the 2024 prize in chemistry to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at the same company, for their work on using artificial intelligence to predict the structures of proteins. The other half goes to David Baker, a professor of biochemistry at the University of Washington, for his work on computational protein design. The winners will share a prize pot of 11 million Swedish kronor ($1 million).
The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure—a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein’s structure, computational tools such as those developed by this year’s award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research on cures for cancer, or lead to completely new materials.
Hassabis and Jumper created AlphaFold, which in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The AI tool has since been used to predict the shapes of all proteins known to science.
“I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people,” said Demis Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” he added.
Baker has created several AI tools for designing and predicting the structure of proteins, such as a family of programs called Rosetta. In 2022, his lab created an open-source AI tool called ProteinMPNN that could help researchers discover previously unknown proteins and design entirely new ones. It helps researchers who have an exact protein structure in mind find amino acid sequences that fold into that shape.
Most recently, in late September, Baker’s lab announced it had developed custom molecules that allow scientists to precisely target and eliminate proteins associated with diseases in living cells.
“[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful,” Baker told MIT Technology Review in 2022.
born 1962 in Seattle, WA, USA. PhD 1989 from University of California, Berkeley, CA, USA. Professor at University of Washington, Seattle, WA, USA and Investigator, Howard Hughes Medical Institute, USA.
University of Washington, Seattle, WA, USA
Howard Hughes Medical Institute, USA
Demis Hassabis “for protein structure prediction”
born 1976 in London, UK. PhD 2009 from University College London, UK. CEO of Google DeepMind, London, UK.
Google DeepMind, London, UK
John M. Jumper “for protein structure prediction”
born 1985 in Little Rock, AR, USA. PhD 2017 from University of Chicago, IL, USA. Senior Research Scientist at Google DeepMind, London, UK.
Google DeepMind, London, UK
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.
“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.
Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.
The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.
In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.
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This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use
In this curation we wish to present two breaking through goals:
Goal 1:
Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer
Goal 2:
Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.
According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.
These eight subcellular pathologies can’t be measured at present time.
In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.
Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases
Glycation
Oxidative Stress
Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
Membrane instability
Inflammation in the gut [mucin layer and tight junctions]
Epigenetics/Methylation
Autophagy [AMPKbeta1 improvement in health span]
Diseases that are not Diseases: no drugs for them, only diet modification will help
Image source
Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease
These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:
Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL
In the Bioelecronics domain we are inspired by the work of the following three research sources:
Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia
THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC
PENDING
THE VOICE of Stephen J. Williams, PhD
Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes
25% of US children have fatty liver
Type II diabetes can be manifested from fatty live with 151 million people worldwide affected moving up to 568 million in 7 years
A common myth is diabetes due to overweight condition driving the metabolic disease
There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
Thirty percent of ‘obese’ people just have high subcutaneous fat. the visceral fat is more problematic
there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects. Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
At any BMI some patients are insulin sensitive while some resistant
Visceral fat accumulation may be more due to chronic stress condition
Fructose can decrease liver mitochondrial function
A methionine and choline deficient diet can lead to rapid NASH development
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
Recent genetic studies have identified variants associated with bipolar disorder (BD), but it remains unclear how brain gene expression is altered in BD and how genetic risk for BD may contribute to these alterations. Here, we obtained transcriptomes from subgenual anterior cingulate cortex and amygdala samples from post-mortem brains of individuals with BD and neurotypical controls, including 511 total samples from 295 unique donors. We examined differential gene expression between cases and controls and the transcriptional effects of BD-associated genetic variants. We found two coexpressed modules that were associated with transcriptional changes in BD: one enriched for immune and inflammatory genes and the other with genes related to the postsynaptic membrane. Over 50% of BD genome-wide significant loci contained significant expression quantitative trait loci (QTL) (eQTL), and these data converged on several individual genes, including SCN2A and GRIN2A. Thus, these data implicate specific genes and pathways that may contribute to the pathology of BP.
Gene Expression Markers for Bipolar Disorder Pinpointed
The work was led by researchers at Johns Hopkins’ Lieber Institute for Brain Development. The findings, published this week in Nature Neuroscience, represent the first time that researchers have been able to apply large-scale genetic research to brain samples from hundreds of patients with bipolar disorder (BD). They used 511 total samples from 295 unique donors.
“This is the first deep dive into the molecular biology of the brain in people who died with bipolar disorder—studying actual genes, not urine, blood or skin samples,” said Thomas Hyde of the Lieber Institute and a lead author of the paper. “If we can figure out the mechanisms behind BD, if we can figure out what’s wrong in the brain, then we can begin to develop new targeted treatments of what has long been a mysterious condition.”
Bipolar disorder is characterized by extreme mood swings, with episodes of mania alternating with episodes of depression. It usually emerges in people in their 20s and 30s and remains with them for life. This condition affects approximately 2.8% of the adult American population, or about 7 million people. Patients face higher rates of suicide, poorer quality of life, and lower productivity than the general population. Some estimates put the annual cost of the condition in the U.S. alone at $219.1 billion.
While drugs can be useful in treating BD, many patients find they have bothersome side effects, and for some patients, current medications don’t work at all.
In this study, researchers measured levels of messenger RNA in the brain samples. They observed almost eight times more differentially expressed gene features in the sACC versus the amygdala, suggesting that the sACC may play an especially prominent role—both in mood regulation in general and BD specifically.
In patients who died with BD, the researchers found abnormalities in two families of genes: one containing genes related to the synapse and the second related to immune and inflammatory function.
“There finally is a study using modern technology and our current understanding of genetics to uncover how the brain is doing,” Hyde said. “We know that BD tends to run in families, and there is strong evidence that there are inherited genetic abnormalities that put an individual at risk for bipolar disorder. Unlike diseases such as sickle-cell anemia, bipolar disorder does not result from a single genetic abnormality. Rather, most patients have inherited a group of variants spread across a number of genes.”
“Bipolar disorder, also known as manic-depressive disorder, is a highly damaging and paradoxical condition,” said Daniel R. Weinberger, chief executive and director of the Lieber Institute and a co-author of the study. “It can make people very productive so they can lead countries and companies, but it can also hurl them into the meat grinder of dysfunction and depression. Patients with BD may live on two hours of sleep a night, saving the world with their abundance of energy, and then become so self-destructive that they spend their family’s fortune in a week and lose all friends as they spiral downward. Bipolar disorder also has some shared genetic links to other psychiatric disorders, such as schizophrenia, and is implicated in overuse of drugs and alcohol.”
@MIT Artificial intelligence system rapidly predicts how two proteins will attach: The model called Equidock, focuses on rigid body docking — which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don’t squeeze or bend
Reporter: Aviva Lev-Ari, PhD, RN
This paper introduces a novel SE(3) equivariant graph matching network, along with a keypoint discovery and alignment approach, for the problem of protein-protein docking, with a novel loss based on optimal transport. The overall consensus is that this is an impactful solution to an important problem, whereby competitive results are achieved without the need for templates, refinement, and are achieved with substantially faster run times.
Keywords:protein complexes, protein structure, rigid body docking, SE(3) equivariance, graph neural networks
Abstract: Protein complex formation is a central problem in biology, being involved in most of the cell’s processes, and essential for applications such as drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no three-dimensional flexibility during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right location and the right orientation relative to the second protein. We mathematically guarantee that the predicted complex is always identical regardless of the initial placements of the two structures, avoiding expensive data augmentation. Our model approximates the binding pocket and predicts the docking pose using keypoint matching and alignment through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements over existing protein docking software and predict qualitatively plausible protein complex structures despite not using heavy sampling, structure refinement, or templates.
One-sentence Summary: We perform rigid protein docking using a novel independent SE(3)-equivariant message passing mechanism that guarantees the same resulting protein complex independent of the initial placement of the two 3D structures.
MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together. Their technique is between 80 and 500 times faster than state-of-the-art software methods, and often predicts protein structures that are closer to actual structures that have been observed experimentally.
This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.
“Deep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and people haven’t found good ways to express them. This deep-learning model can learn these types of interactions from data,” says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.
Ganea’s co-lead author is Xinyuan Huang, a graduate student at ETH Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering in CSAIL and a member of the Institute for Data, Systems, and Society. The research will be presented at the International Conference on Learning Representations.
Significance of the Scientific Development by the @MIT Team
EquiDock wide applicability:
Our method can be integrated end-to-end to boost the quality of other models (see above discussion on runtime importance). Examples are predicting functions of protein complexes [3] or their binding affinity [5], de novo generation of proteins binding to specific targets (e.g., antibodies [6]), modeling back-bone and side-chain flexibility [4], or devising methods for non-binary multimers. See the updated discussion in the “Conclusion” section of our paper.
Advantages over previous methods:
Our method does not rely on templates or heavy candidate sampling [7], aiming at the ambitious goal of predicting the complex pose directly. This should be interpreted in terms of generalization (to unseen structures) and scalability capabilities of docking models, as well as their applicability to various other tasks (discussed above).
Our method obtains a competitive quality without explicitly using previous geometric (e.g., 3D Zernike descriptors [8]) or chemical (e.g., hydrophilic information) features [3]. Future EquiDock extensions would find creative ways to leverage these different signals and, thus, obtain more improvements.
Novelty of theory:
Our work is the first to formalize the notion of pairwise independent SE(3)-equivariance. Previous work (e.g., [9,10]) has incorporated only single object Euclidean-equivariances into deep learning models. For tasks such as docking and binding of biological objects, it is crucial that models understand the concept of multi-independent Euclidean equivariances.
All propositions in Section 3 are our novel theoretical contributions.
We have rewritten the Contribution and Related Work sections to clarify this aspect.
Footnote [a]: We have fixed an important bug in the cross-attention code. We have done a more extensive hyperparameter search and understood that layer normalization is crucial in layers used in Eqs. 5 and 9, but not on the h embeddings as it was originally shown in Eq. 10. We have seen benefits from training our models with a longer patience in the early stopping criteria (30 epochs for DIPS and 150 epochs for DB5). Increasing the learning rate to 2e-4 is important to speed-up training. Using an intersection loss weight of 10 leads to improved results compared to the default of 1.
Bibliography:
[1] Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration, Hassan et al., 2017
[2] GNINA 1.0: molecular docking with deep learning, McNutt et al., 2021
[3] Protein-protein and domain-domain interactions, Kangueane and Nilofer, 2018
[4] Side-chain Packing Using SE(3)-Transformer, Jindal et al., 2022
[5] Contacts-based prediction of binding affinity in protein–protein complexes, Vangone et al., 2015
[6] Iterative refinement graph neural network for antibody sequence-structure co-design, Jin et al., 2021
[7] Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes, Eismann et al, 2020
[8] Protein-protein docking using region-based 3D Zernike descriptors, Venkatraman et al., 2009
[9] SE(3)-transformers: 3D roto-translation equivariant attention networks, Fuchs et al, 2020
[10] E(n) equivariant graph neural networks, Satorras et al., 2021
[11] Fast end-to-end learning on protein surfaces, Sverrisson et al., 2020
2012pharmaceutical
sjwilliamspa
This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://bit.ly/4diKiJ2