Tag: molecular biology

  • How migrating cells define their back to move forward

    How migrating cells define their back to move forward

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    • RESEARCH BRIEFINGS

    Migrating cells must determine where to form a front end and a back end. A gradient in contact sites between the cell membrane and an organelle called the endoplasmic reticulum (ER) is generated by a gradient of ER curvature and is required to guide and control migration speed.

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  • New Study Unfolds the Electric Mystery of Peptides

    New Study Unfolds the Electric Mystery of Peptides

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    Electron Transport Illustration

    Electron transport, the energy-generating process inside living cells that enables photosynthesis and respiration, is enhanced in peptides with a collapsed, folded structure. Credit: Moeen Meigooni.

    Researchers validated their findings, which were published in PNAS, using a combination of single-molecule experiments, molecular dynamics simulations, and quantum mechanics.

    A new study reveals that peptides with a folded structure conduct electricity better than their unfolded counterparts. Researchers at the Beckman Institute used experiments and simulations to demonstrate how these structures influence electron transport, crucial for processes like photosynthesis and respiration. This finding not only deepens our understanding of electron flow in complex molecular structures but also opens new possibilities for developing advanced molecular electronic devices.

    What puts the electronic pep in peptides? A folded structure, according to a new study in the Proceedings of the National Academy of Sciences.

    Electron transport, the energy-generating process inside living cells that enables photosynthesis and respiration, is enhanced in peptides with a collapsed, folded structure. Interdisciplinary researchers at the Beckman Institute for Advanced Science and Technology combined single-molecule experiments, molecular dynamics simulations, and quantum mechanics to validate their findings.

    “This discovery provides a new understanding of how electrons flow through peptides with more complex structures while offering new avenues to design and develop more efficient molecular electronic devices,” said lead investigator Charles Schroeder, the James Economy Professor in Materials Science and Engineering at the University of Illinois Urbana-Champaign.

    Proteins reside in all living cells and are integral to cellular activities like photosynthesis, respiration (taking in oxygen and expelling carbon dioxide), and muscle contraction.

    Chemically, proteins are long sequences of amino acids strung like holiday lights, the different colors representing different amino acids like tryptophan and glutamine.

    In a protein’s simplest form (its primary structure) the amino acid string lies flat. But amino acids are prone to mingling; when they interact with one another, the string tangles, causing the structural collapse referred to as protein folding (or secondary structure).

    The researchers asked if and how a protein’s structure impacts its ability to conduct electricity — a question not clearly answered by existing literature.

    Research Focus on Peptides

    Rajarshi “Reeju” Samajdar, a graduate student in the Schroeder Group, was patiently probing this protein problem by experimenting on one molecule at a time. But Samajdar was not looking at proteins at all. Instead, he focused on peptides, fragments of proteins with a fraction of the amino acids. For this study, Samajdar used peptides with about four or five amino acids, which permitted more granular observation, he said.

    Samajdar saw something surprising: stretched-out peptides with a primary structure seemed to be less effective energy conductors than their folded counterparts with a secondary structure. The stark difference between the peptides’ behavior in each state piqued his curiosity.

    “Peptides are very flexible. We were interested in understanding how the conductance properties changed as you stretch them out and the peptides transition from a folded secondary structure to an extended conformation. Interestingly, I saw a distinct jump between those two structures, with different electronic properties in each,” Samajdar said.

    To verify his observations, Samajdar called on Moeen Meigooni, a graduate research assistant working with Emad Tajkhorshid, a Beckman researcher, professor and the J. Woodland Hastings Endowed Chair in Biochemistry.

    The team simulated the peptides’ conformational behavior with computer modeling, confirming the jerky structural shifts Samajdar observed. Leaving no scientific stones unturned, the researchers worked with Martin Mosquera, an assistant professor of chemistry at Montana State University, and Nicholas Jackson, a Beckman researcher and an assistant professor of chemistry at Illinois, to use quantum mechanical calculations to confirm that these two discrete structures were indeed linked to the changes in conductivity.

    “We believe that our approach combining single-molecule experiments, structural modeling with molecular dynamics and quantum mechanics is a very powerful approach for understanding molecular electronics,” Samajdar said. “We could have gone straight to quantum, but we didn’t. The computer simulation piece allowed us to study the entire conformational space of the peptides.”

    The researchers’ triple-checked results indicate that peptides with a folded secondary structure do conduct electricity better than peptides with an unfolded primary structure. The specific secondary structure they observed formed a shape called the 310 helix.

    Because this work was conducted on peptides, the results lend themselves to a greater understanding of electron transport in larger, more complex proteins and other biomolecules, pointing toward applications in molecular electronic devices like semiconductors that work by switching between two distinct structures.

    Reference: “Secondary structure determines electron transport in peptides” by Rajarshi Samajdar, Moeen Meigooni, Hao Yang, Jialing Li, Xiaolin Liu, Nicholas E. Jackson, Martín A. Mosquera, Emad Tajkhorshid and Charles M. Schroeder, 25 July 2024, Proceedings of the National Academy of Sciences.
    DOI: 10.1073/pnas.2403324121



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  • Bizarre bacteria defy textbooks by writing new genes

    Bizarre bacteria defy textbooks by writing new genes

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    A computer rendered illstration of an RNA molecule.

    A bacterial enzyme turns biology on its head by reading RNA (artist’s illustration) into DNA that forms new genes.Credit: Artur Plawgo/Science Photo Library

    Genetic information usually travels down a one-way street: genes written in DNA serve as the template for making RNA molecules, which are then translated into proteins. That tidy textbook story got a bit complicated in 1970 when scientists discovered that some viruses have enzymes called reverse transcriptases, which scribe RNA into DNA — the reverse of the usual traffic flow.

    Now, scientists have discovered an even weirder twist1. A bacterial version of reverse transcriptase reads RNA as a template to make completely new genes written in DNA. These genes are then transcribed back into RNA, which is translated into protective proteins when a bacterium is infected by a virus. By contrast, viral reverse transcriptases don’t make new genes; they merely transfer information from RNA to DNA.

    “This is crazy molecular biology,” says Aude Bernheim, a bioinformatician at the Pasteur Institute in Paris, who was not involved in the research. “I would have never guessed this type of mechanism existed.”

    One-up on CRISPR

    Bacteria fend off viruses and other invaders by deploying myriad defences, such as the juggernaut gene-editing system CRISPR. One of the more mysterious defence systems contains the DNA gene for a reverse transcriptase and a short stretch of mysterious RNA without any clear function: the sequence didn’t seem to encode any protein.

    To work out how this system works, a team co-led by molecular biologist Stephen Tang and biochemist Samuel Sternberg, both at Columbia University in New York City, searched for the DNA molecules made by a reverse transcriptase from bacteria called Klebsiella pneumoniae. It found very long DNA sequences that consisted of numerous identical repeating segments. Each segment matched a chunk of the mysterious RNA.

    Loop-the-loop

    To explain this, the authors note that long RNA strands can form hairpin-like shapes, bringing two distant portions close to each other. The researchers found that the K. pneumoniae reverse transcriptase was doing repeated ‘laps’ around the RNA sequence, which was looped over itself like a shoelace, writing the same RNA molecule into DNA many times over. This created a repetitive DNA sequence.

    The repeated segments created a protein-coding sequence called an open reading frame. The researchers named this sequence neo, for ‘never-ending open reading frame’, because it lacks a sequence that signals the end of a protein and, therefore, theoretically has no limit. They then found that viral infection triggers the production of the Neo protein, which causes cells to stop dividing. The findings, which have not yet been peer reviewed, were posted to the bioRxiv preprint server on 8 May.

    How Neo halts growth of infected cells isn’t yet clear, the researchers say. A predicted 3D structure of a portion of Neo — its length probably varies depending on how much of its RNA gets translated — suggests that it forms a series of helices. Experiments showed that breaking up these shapes stymied Neo’s toxic effects. Exactly how viral infection kicks off the creation of the Neo protein is also a mystery, says Bernheim. “This I am burning to know.”

    Wonderful life

    The discovery that reverse transcriptase — which has previously been known only for copying genetic material — can create completely new genes has left other researchers gobsmacked. “This looks like biology from alien organisms,” Israel Fernandez, a computational chemist at Complutense University of Madrid, wrote on X.

    “Their findings were astonishing,” says Nicolás Toro García, a molecular biologist at Zaidín Experimental Research Station in Grenada, Spain, and should help researchers to develop biotechnology applications for the system.

    The discovery has even left Sternberg in awe: “It should change the way we look at the genome.”

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  • Unveiling Secrets of Early Life With Synthetic Models

    Unveiling Secrets of Early Life With Synthetic Models

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    By

    Membrane Cell Biology Concept

    Researchers from the Okinawa Institute of Science and Technology created synthetic droplets to study chemotaxis, mimicking cellular movement by responding to chemical gradients which could explain early life movement and inspire future technologies. Their findings, using droplets that migrate toward each other due to changes in pH and surface tension, contribute to understanding fundamental biological processes and the potential development of new biotechnological applications.

    A synthetic droplet could provide researchers with insights into how the most basic forms of life on Earth might interact with their environment.

    Our bodies consist of trillions of diverse cells, each performing a specific role to sustain our lives.

    How do cells move around inside these extremely complicated systems? How do they know where to go? And how did they get so complicated to begin with? Simple yet profound questions like these are at the heart of curiosity-driven basic research, which focuses on the fundamental principles of natural phenomena. An important example is the process by which cells or organisms move in response to chemical signals in their environment, also known as chemotaxis.

    Three Figures Showing the Principles of Droplet Movement Caused by the Marangoni Flow

    The synthetic droplets contain the enzyme urease which catalyzes the breakdown of urea into ammonia, which has a high pH-value. Droplets migrate due to the pH gradient, from low to high, because of the Marangoni effect. Credit: OIST

    A constellation of researchers from three different research units at the Okinawa Institute of Science and Technology (OIST) came together to answer basic questions about chemotaxis by creating synthetic droplets to mimic the phenomena in the lab, allowing them to precisely isolate, control, and study the phenomena. Their results, which helps answering questions about the principles of movement in simple biological systems, have now been published in the Journal of The American Chemical Society.

    “We have shown that it is possible to make protein droplets migrate through simple chemical interactions,” says Alessandro Bevilacqua, PhD student in the Protein Engineering and Evolution Unit and co-first author on the paper. Professor Paola Laurino, head of the unit and leading author, adds that they “have created a simple system that mimic a very complex phenomenon, and which can be modulated through enzymatic activity.”


    Numerical models showing what happens when the halos of two synthetic droplets interact. pH in the space between the droplets is higher (and surface tension lower), which causes the droplets to migrate towards each other while keeping their spherical shape, as pH is lower within the droplets, until they meet and merge. Larger droplets attract smaller droplets.
    Credit: OIST

    Tensions on the surface

    While the process of creating droplets might not sound like the most complicated task, mimicking biological processes as close to reality as possible while keeping accurate control over all the variables certainly is. The synthetic, membrane-less droplets contain a very high concentration of the bovine protein BSA to mimic the crowded conditions inside cells, as well as urease, an enzyme that catalyzes the breakdown of urea into ammonia.

    Ammonia is basic, meaning it has a high pH-value. As the enzyme gradually catalyzes the production of ammonia, it diffuses into the solution, creating a ‘halo’ of higher pH around the droplet, which in turn enables droplets to detect other droplets and migrate towards each other.

    The researchers found that the key to understanding the chemotaxis of the droplets is the pH-gradient, as it facilitates the Marangoni effect, which describes how molecules flow from areas of high surface tension to low. Surface tension is the measure of energy required to keep molecules at the surface together, like glue. When pH increases, this glue weakens, causing molecules to spread out and lowering surface tension, which in turn makes it easier for molecules to move. You can see this by adding soap, which has a high pH, to one end of a bathtub of still water: the water will flow towards the end with soap because of the Marangoni effect.


    How do the droplets move, and what determines their direction? Each green droplet is densely packed with proteins as well as an enzyme that increases the pH-value within and around the droplet, which may lead to the answer to these questions. Credit: OIST

    When two synthetic droplets are close enough, their halos interact, raising the pH in the environment between them, which makes them move together. Because the surface tension is still strong on the opposite ends of the droplets, they keep their shape until the surfaces touch, and the cohesive forces within the droplets overcome the surface tension, causing them to merge. As larger droplets both produce more ammonia and have a larger surface area (which decreases surface tension), they attract droplets smaller than themselves.

    Collaborating on ancient soup and future biotech

    Thanks to the development of these droplets, the researchers have made headway in answering basic questions about biological movement – and in doing so, they have gained insight into the directed movement of the earliest forms of life in the primordial soup billions of years ago, as well as a lead on creating new biologically inspired materials.

    Our knowledge of life as it looked billions of years ago is fuzzy at best. A prominent hypothesis is that life originated in the oceans, as organic molecules gradually assembled and became more sophisticated in a ‘primordial soup’ – and this could have been facilitated by chemotaxis through the Marangoni effect. “It would have been beneficial for droplets to have this mechanism of migration in the hypothetical origin of life scenario,” as Professor Laurino puts it. This migration could have triggered the formation of primitive metabolic pathways whereby enzymes catalyze a variety of substances that ultimately produce a chemical gradient that drives the droplets together, leading to larger and more sophisticated communities.

    The research also points ahead in time, providing leads on new technology. “One example is the creation of responsive materials inspired by biology,” suggests Alessandro Bevilacqua. “We have shown how simple droplets can migrate thanks to a chemical gradient. A future application of this could be technologies that sense or react to chemical gradients, for example in micro-robotics or drug delivery.”

    The work to produce and analyze the synthetic droplets is the result of a combination of deeply integrated interdisciplinarity and the human factors undergirding scientific work. The project began during the coronavirus pandemic, when a member of the Protein Engineering and Evolution Unit was in quarantine with a member of the Complex Fluids and Flows Unit. The two began talking, and though the two units are from two disparate fields – biochemistry and mechanics, respectively – the project evolved in tandem. Eventually, members from the Micro/Bio/Nanofluidics Unit joined the project with sophisticated measurements of the droplets’ surface tension.

    The unique non-disciplinary research environment at OIST catalyzed the collaboration. As Professor Laurino puts it, “this project could never have existed if we were separated by departments. It hasn’t been an easy collaboration, because we communicate our field in very different ways – but being physically close made it significantly easier.”

    Alessandro Bevilacqua joins in: “The coffee factor has been very important. Being able to sit down with other unit members made the process much faster and more productive.” Their cooperation doesn’t stop here – rather, this paper is the beginning of a fruitful partnership between the three units. “We see a lot of synergy in our work, and we work effectively and efficiently together. I don’t see a reason why we should stop,” as Professor Laurino states it. It’s thanks to the combined efforts of the three units that we now know more about the minute movements of life at the smallest, earliest, and possibly future scale.

    Reference: “Chemotactic Interactions Drive Migration of Membraneless Active Droplets” by Mirco Dindo, Alessandro Bevilacqua, Giovanni Soligo, Vincenzo Calabrese, Alessandro Monti, Amy Q. Shen, Marco Edoardo Rosti and Paola Laurino, 15 April 2024, Journal of the American Chemical Society.
    DOI: 10.1021/jacs.4c02823

    The study was funded by the Japan Society for the Promotion of Science, the Okinawa Institute of Science and Technology Graduate University, the Takeda Foundation, and the High-Performance Computing Infrastructure.



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  • Endurance exercise causes a multi-organ full-body molecular reaction

    Endurance exercise causes a multi-organ full-body molecular reaction

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    • RESEARCH BRIEFINGS

    A study of male and female rats has examined the biomolecular changes induced in many of their organs by eight weeks of endurance treadmill training. The findings offer insights into the many benefits to our immune, metabolic and stress-response pathways as we adapt to exercise.

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  • Dad’s microbiome can affect offspring’s health — in mice

    Dad’s microbiome can affect offspring’s health — in mice

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    Download the Nature Podcast 1 May 2024

    In this episode:

    00:46 Using genomics to explain geographical differences in cancer risk

    The risk of developing cancer can vary hugely depending on geographical region, but it’s not exactly clear why. To get a better idea, a team has compared the genomes of kidney cancers taken from people around the globe. They reveal a link between geographical locations and specific genetic mutations, suggesting that there are as-yet unknown environmental or chemical exposures in different locations. They hope this work will inform public health efforts to identify and reduce potential causes of cancer.

    Research Article: Senkin et al.

    News and Views: Genomics reveal unknown mutation-promoting agents at global sites

    07:46 Research Highlights

    Research reveals that the extinct ‘sabre-toothed salmon’ actually had tusks, and a common fungus that can clean up both heavy-metal and organic pollutants.

    Research Highlight: This giant extinct salmon had tusks like a warthog

    Research Highlight: Garden-variety fungus is an expert at environmental clean-ups

    09:55 How disrupting a male mouse’s microbiome affects its offspring

    Disruption of the gut microbiota has been linked to issues with multiple organs. Now a team shows disruption can even affect offspring. Male mice given antibiotics targeting gut microbes showed changes to their testes and sperm, which led to their offspring having a higher probability of severe growth issues and premature death. Although it’s unknown whether a similar effect would be seen in humans, it suggests that factors other than genetics play a role in intergenerational disease susceptibility.

    Research article: Argaw-Denboba et al.

    News and Views: Dad’s gut microbes matter for pregnancy health and baby’s growth

    17:23 Briefing Chat

    An updated atlas of the Moon that was a decade in the making, and using AI to design new gene-editing systems.

    Nature News: China’s Moon atlas is the most detailed ever made

    Nature News: ‘ChatGPT for CRISPR’ creates new gene-editing tools

    Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.

    Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Spotify, YouTube Music or your favourite podcast app. An RSS feed for the Nature Podcast is available too.

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  • Tumours form without genetic mutations

    Tumours form without genetic mutations

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    Nature, Published online: 24 April 2024; doi:10.1038/d41586-024-01019-2

    Researchers find that brief and reversible inhibition of a gene-silencing mechanism leads to irreversible tumour formation in fruit flies, challenging the idea that cancer is caused only by permanent changes to DNA.

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  • SciY launches new BioDrive tool and registration module for enhanced data management and collaboration in molecular biology research

    SciY launches new BioDrive tool and registration module for enhanced data management and collaboration in molecular biology research

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    The new Arxspan BioDrive is a tool for molecular biology that provides cross-functional research teams with a single, integrated data management platform. With Arxspan BioDrive, users have the ability to create, import, and export nucleotide and amino acid sequences in various file formats, import sequences directly from NCBI’s database, design primers using the Primer Design Tool, and maintain relationships between molecules. Additionally, users can align designed sequences with one or more sequencing files, plan cloning projects, and complete their biological workflow seamlessly.

    Molecular Biology Research: The New BioDrive by SciY

    SciY has also launched a new version of Arxspan’s Registration Module. The enhanced Registration Module provides a comprehensive solution for managing molecular biology, biology, and chemistry data all from a single platform. It features user-customizable fields, flexible parent and child naming and/or numbering, integrated chemistry drawing tools, and advanced search capabilities. The Registration Module, with its seamless integration with the new BioDrive and the Electronic Laboratory Notebook (ELN), allows users to eliminate data silos that hinder data sharing across project teams, thereby enabling a more efficient and effective completion of biological workflows.

    “We are excited to launch the new BioDrive and the new Registration Module to provide cross-functional research teams with a complete solution for managing molecular biology, biology, and chemistry data from a single platform. Our goal is to help scientists be more efficient and productive in their research by simplifying the data management process,” said Guy Desmarquets, Senior Director of Sales and Business Development, SciY.

    BioDrive and the Registration module are available now and can be accessed through Arxspan by SciY cloud-based scientific informatics solutions.

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  • Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

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    µOR expression and purification

    The wild-type Mus musculus µOR (6-398) with an N-terminal HA signal sequence followed by a Flag tag and a C-terminal 8×His tag was cloned in the pFastBac1 vector. The minimal-cysteine construct (µOR∆7) was created by introducing the mutations51 C13S, C22S, C43S, C57S, C170T, C346A and C351L into the wild-type µOR. Double-cysteine mutation constructs (µOR∆7(R182C/R276C) for DEER, µOR∆7(T180C/R276C) and µOR∆7(R182C/R273C) for smFRET experiments) were generated based on the µOR∆7 construct. The µOR was expressed and purified following a previous protocol13 with some modifications. The µOR was expressed in Sf9 insect cells (Expression Systems, authenticated by supplier, not tested for mycoplasma) using Bac-to-Bac baculovirus systems with 10 µM naloxone. Cells were collected 48 h post infection and were lysed in a buffer of 10 mM Tris pH 7.5, 1 mM EDTA, 100 µM TCEP, 10 µM naloxone, 160 µg ml−1 benzamidine and 2.5 µg ml−1 leupeptin. The receptor was extracted from the Sf9 membrane using buffer of 20 mM HEPES pH 7.5, 500 mM NaCl, 0.7% N-dodecyl-β-d-maltoside (DDM), 0.3% CHAPS, 0.03% cholesteryl hemisuccinate (CHS), 30% (v/v) glycerol, 5 mM imidazole, 2 mM MgCl2, 160 µg ml−1 benzamidine, 2.5 µg ml−1 leupeptin, 10 µM naloxone, 100 µM TCEP and 2 µl benzonase in the cold room for 1 h. After centrifugation, Ni-NTA resin was added to the supernatant in a 500-ml centrifuge tube (Corning) and rotated for 2 h at 4 °C. Ni-NTA resin was washed in batch with washing buffer of 20 mM HEPES pH 7.5, 500 mM NaCl, 0.1% DDM, 0.03% CHAPS, 0.03% CHS, 5 mM imidazole and 10 µM naloxone and protein was eluted in washing buffer supplemented with 250 mM imidazole. Ni-NTA eluate was supplemented with 2 mM CaCl2 and loaded onto anti-Flag M1 resin (Millipore-Sigma) for further purification. The detergent was exchanged to LMNG on a Flag column by gradually increasing the proportion of the exchange buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 0.5 LMNG, 0.05% CHS, 2 mM CaCl2 and 10 µM naloxone) over the Ni-NTA washing buffer supplemented with 2 mM CaCl2 at room temperature for 1 h. The µOR was finally eluted with buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 5 mM EDTA, 0.2 mg ml−1 Flag peptide and 10 µM naloxone. After concentrating with a 4-ml 100-kDa cutoff concentrator (Amicon Ultra), the µOR was further purified by size-exclusion chromatography (SEC) using an SD200 increase 10/300 column (GE Healthcare) equilibrated with SEC buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS and 10 µM naloxone. Fractions containing monomeric µOR were collected and concentrated with a 500-µl 100-kDa cutoff concentrator (Amicon Ultra). The µOR was supplemented with 15% (v/v) glycerol and flash frozen in liquid nitrogen.

    Gi heterotrimer expression and purification

    DNA for the human Gαi1 was cloned into the pFastBac1 vector. DNA of human Gβ1 with an N-terminal 6×His tag and HRV 3C protease cleavage site (LEVLFQGP) and Gγ2 were cloned into the vector of pFastBac Dual under the promoter of ph and p10, respectively. P2 viruses of Gαi1 and Gβ1γ2 were generated following the same protocol for the µOR. Gi1 heterotrimer was expressed in Hi5 cells (Expression Systems, authenticated by supplier, not tested for mycoplasma) with 4 ml P2 of Gαi1 and 10 ml P2 of Gβ1γ2 per liter cells when cells reached a density of 3 million per ml. Cells were collected 48 h post infection and kept in −80 °C freezer until use.

    Cell pellets were thawed in lysis buffer (10 mM Tris pH 7.5, 1 mM MgCl2, 5 mM β-mercaptoethanol (β-ME), 10 µM GDP, 160 µg ml−1 benzamidine, 2.5 µg ml−1 leupeptin). After centrifugation, pellets were solubilized in solubilization buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 1% sodium cholate, 0.05% LMNG, 5 mM MgCl2, 20 mM imidazole, 5 mM β-ME, 10 µM GDP, 160 µg ml−1 benzamidine, 2.5 µg ml−1 leupeptin) and were stirred in a cold room for 1 h. After centrifugation at 14,000 rpm for 20 min, the supernatant was mixed with Ni-NTA resin and rotated at 4 °C for 1 h. Ni-NTA resin was then washed four times in batch with solubilization buffer. Detergent was exchanged to LMNG on the Ni-NTA column by gradually increasing LMNG concentration at room temperature. Protein was eluted with elution buffer (20 mM HEPES pH 7.5, 50 mM NaCl, 0.01% LMNG, 2 mM MgCl2, 5 mM β-ME, 10 µM GDP, 180 mM imidazole). The His tag was cleaved by 1:50 (w/w) HRC 3 C protease. Gi1 was treated with 5 µl of λ protein phosphatase and was dialysed against dialysis buffer (20 mM HEPES pH 7.5, 50 mM NaCl, 0.01% LMNG, 2 mM MgCl2, 2 mM MnCl2, 5 mM β-ME, 10 µM GDP) overnight at 4 °C to remove imidazole. The His tag and contaminates were removed by loading Gi1 onto 2-ml Ni-NTA resin. Flow-through of Ni-NTA resin was loaded onto a MonoQ column and Gi1 was further purified by anion exchange. The Gi1 heterotrimer peak was collected and concentrated. After being supplemented with 15% glycerol, Gi1 was flash froze and kept in −80 °C freezer. For DEER samples, ion-exchange purified Gi1 was further injected onto an SD200 increase 10/300 column (GE Healthcare) equilibrated with SEC buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 2 mM MgCl2 and 10 µM GDP). SEC fractions were pooled, concentrated to 336 µM and flash frozen.

    GRK5 expression and purification

    Human GRK5 DNA with a C-terminal 6×His tag was cloned into pFastBac1 vector. P2 virus was generated following the same protocol of the µOR. GRK5 was expressed in Sf9 insect cells with 25 ml of P2 virus and was collected 48 h after infection. Purification of GRK5 was performed on ice or at 4 °C. Cells were lysed in lysis buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 20 mM imidazole, 5 mM β-ME, 160 µg ml−1 benzamidine, 2.5 µg ml−1 leupeptin) by sonication on ice. Cell debris was removed by centrifuge at 14,000 rpm for 20 min. GRK5 in supernatant was purified by Ni-NTA resin using wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 20 mM imidazole, 5 mM β-ME). Protein was eluted in wash buffer supplemented with 160 mM imidazole. GRK5 was concentrated and injected in an SD200 increase 10/300 column equilibrated with cold SEC buffer (20 mM HEPES pH 7.5, 300 mM NaCl) in cold room. SEC fractions of GRK5 were pooled, concentrated and flash frozen.

    β-Arrestin-1 expression and purification

    To investigate the conformational changes of the µOR in the presence of β-arrestin-1, a C-terminal truncated β-arrestin-1 was used for smFRET and DEER measurements. The long splice variant of human, cysteine-free (C59V/C125S/C140L/C150V/C242V/C251V/C269S), truncated β-arrestin-1 (1-382) (βarr1(∆CT))52 with an N-terminal 6×His and HRV 3 C site was in vector of pET15b and was transformed into BL21 (DE3) competent cells. Escherichia coli cells were cultured in TB medium with 100 µg ml−1 ampicillin until OD600 reached 1.2 at 37 °C in a shaker at 220 rpm. The temperature was decreased to 18 °C and protein expression was induced with 200 µM IPTG for 16 h. Purification of βarr1(∆CT) was performed on ice or at 4 °C. Cells were collected and sonicated in buffer 1 (20 mM Tris 8.0 (25 °C), 300 mM NaCl, 20 mM imidazole) supplemented with 160 µg ml−1 benzamidine and 2.5 µg ml−1 leupeptin. After centrifugation, protein in the supernatant was incubated with Ni-NTA resin at 4 °C for 1 h. The Ni-NTA resin was extensively washed with buffer 1, then was further washed with 3 column volumes of buffer 2 (20 mM Tris 8.0 (25 °C), 50 mM NaCl and 20 mM imidazole). βarr1(∆CT) was eluted with buffer 2 supplemented with 160 mM imidazole. βarr1(∆CT) was loaded onto a Source 15Q 4.6/100 PE anion-exchange column (GE Healthcare). The column was washed with 2 column volumes of buffer A (20 mM Tris 8.0 (25 °C), 50 mM NaCl), and βarr1(∆CT) was eluted with 15 column volumes of a linear gradient from 0 to 30% buffer B (20 mM Tris 8.0 (25 °C), 1 M NaCl). The peak fractions were pooled and supplemented with NaCl to a final concentration of 300 mM, which prevented the protein from precipitating when concentrated to high concentration in the following step. The protein was concentrated and injected in an SD200 increase 10/300 column equilibrated with SEC buffer of 20 mM HEPES pH 7.5, 300 mM NaCl. For DEER samples, SEC buffer was made in D2O, and βarr1(∆CT) was concentrated to 986 µM and flash frozen.

    Phosphorylation of µOR

    The µOR was purified following the standard µOR purification protocol except that the naloxone was replaced with 10 µM DAMGO on the anti-Flag M1 resin and SEC purification procedures. 4 µM of µOR∆7(R182C/R276C) purified in the presence of DAMGO was incubated in phosphorylation buffer of 20 mM HEPES pH 7.5, 35 mM NaCl, 5 mM MgCl2, 100 µM TCEP, 20 µM 1,2-dioctanoyl-sn-glycero-3-phospho-(1′-myo-inositol-4′,5′-bisphosphate) (C8-PIP2), 0.01% LMNG, 0.001% CHS and 100 µM DAMGO at room temperature for 1 h. ATP and GRK5 were then added to the reaction to a final concentration of 1 mM and 0.8 µM, respectively, and incubated for 1 h before more GRK5 was added. GRK5 was added every 1 h four times in total and the reaction was kept at room temperature.

    To evaluate the phosphorylation level and make sure it reaches completion using ion-exchange chromatography, 12 µl of the phosphorylation reaction containing about 50 picomoles of µOR at different time points was removed and diluted to 200 µl using the buffer of 20 mM Tris pH 8.0 (25 °C), 50 mM NaCl, 0.01% LMNG, 5 mM EDTA and 10 µM naloxone. The samples were then injected onto a MonoQ (5/50) anion-exchange column (GE Healthcare) equilibrated with buffer A of 20 mM Tris 8.0 (25 °C), 50 mM NaCl, 0.01% LMNG and 10 µM naloxone. The column was washed with 1 column volumes of buffer A, and then with 40 column volumes of a linear gradient from 0 to 40% buffer B of 20 mM Tris 8.0 (25 °C), 1 M NaCl, 0.01% LMNG and 10 µM naloxone at room temperature. Protein elution was monitored by a fluorescence detector (Shimadzu) with excitation at 280 nm and emission at 340 nm (Extended Data Fig. 12a).

    After the 4-h incubation with GRK5, the reaction was diluted by tenfold with the wash buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 2 mM CaCl2 and 10 µM naloxone before loading onto 3 ml M1 resin. The M1 resin was washed with 30 ml of the wash buffer at room temperature for 30 min. The µOR was finally eluted using elution buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 10 µM naloxone, 5 mM EDTA and 0.2 mg ml−1 Flag peptide. After concentration, the µOR was further injected onto an SD200 increase 10/300 column equilibrated with SEC buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS and 10 µM naloxone. Fractions containing monomeric µOR were collected and concentrated with a 500-µl 100-kDa cutoff concentrator (Amicon Ultra). The µOR was supplemented with 15% (v/v) glycerol and flash frozen in liquid nitrogen.

    Fluorophore synthesis

    Iodoacetamide-conjugated Cy3 and Cy5 fluorophores were synthesized following a previous protocol30. In brief, 1 µmol of sulfo-Cyanine3 NHS ester or sulfo-Cyanine5 NHS ester (Lumiprobe) was dissolved in 500 μl dry dimethyl sulfoxide (DMSO). It was then added dropwise to a solution of 50 μl cadaverine in 500 μl of dry DMSO at room temperature. The reaction solution was stirred at room temperature for 5 min, then poured into 15 ml of 5% formic acid in ethyl acetate. The precipitate was collected and purified by high-performance liquid chromatography using 10 mM triethylammonium acetate pH 7.0 aqueous buffer (solvent A) with 100% acetonitrile (solvent B) as the mobile phase. The product fraction was dried using a rotary evaporator. The resulting pure fluorophore–cadaverine compound was then dissolved in 1 ml dry DMSO. N,N-diisopropylethylamine (100 μl) was added to this solution, followed by 1 mg iodoacetic acid NHS ester. The reaction solution was stirred at room temperature for 15 min and then poured into 15 ml ethyl acetate. The precipitate was collected and purified by high-performance liquid chromatography.

    Synthesis of HO-1427

    The bromo derivative53 (261 mg, 1.0 mmol) (HO-559) was dissolved in acetone (20 ml) and NaI (300 mg, 2 mmol) was added. The reaction mixture was refluxed for 1 h then evaporated. The residue was dissolved in ethyl acetate/diethyl ether (50:50, 20 ml) and washed with brine (2 × 10 ml). The organic phase was dried (MgSO4), filtered, evaporated and purified with flash chormatography (hexane:diethyl ether) yielding yellow crystals 230 mg (74%); melting point: 132–134 °C; retention factor (Rf) = 0.4 (hexane:ethyl acetate 2:1); Elemental analysis calculated for C10H15INO2 (Mw: 308.1) C: 38.98; H: 4.91; N: 4.55%; measured: C: 39.02; H: 4.78; N: 4.61%; IR (cm−1): 1665, 1615; MS (EI, m/z,%): 308 (8), 294 (6), 278 (6), 151 (100), 136 (8), 109 (52), 43 (61).

    The melting point was measured with a Boetius micro melting point apparatus. The infrared (IR) spectrum was obtained using a Bruker Alpha FT-IR instrument with an attenuated total reflectance support on a diamond plate. The mass spectrum was recorded on a Shimadzu GCMS-2020 spectrometer in electron ionization (EI) mode (70 eV). The elemental analysis was performed on a Fisons EA 1110 CHNS instrument. Flash column chromatography was performed on Merck Kieselgel 60 (0.040–0.063 mm) column. Qualitative thin layer chromatography (TLC) was carried out on commercially available plates (20 cm × 20 cm × 0.02 cm) coated with Merck Kieselgel.

    µOR labelling with fluorophores

    Minimal-cysteine µOR with cysteine mutations on TM4 and TM6, namely µOR∆7(T180C/R276C) and µOR∆7(R182C/R273C), was labelled by commercial maleimide-conjugated sulfo-Cy3 and sulfo-Cy7 (Lumiprobe) or by home-made iodoacetamide-conjugated Cy3 and Cy5, respectively. SEC purified µOR was diluted to 10 µM in 20 µl of labelling buffer (50 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 10 µM naloxone). 30 µM of donor fluorophore and 60 µM of acceptor fluorophore were added into the reaction. After incubation at 20 °C for 30 min, free dyes were quenched with 10 mM l-cysteine. The reaction was then loaded onto a home-packed desalt column filled with 2-ml G50 resin (Sigma) equilibrated with the desalt buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 15% glycerol). Fractions containing µOR were pooled, aliquoted and flash frozen. The concentration of µOR was approximately 500 nM.

    µOR labelling with nitroxide spin label

    To make samples of the µOR alone or in complex with G protein for DEER studies, SEC purified µOR∆7(R182C/R276C) without phosphorylation was diluted to 20 µM in labelling buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 10 µM naloxone). Nitroxide spin label reagent HO-1427 was added to a final concentration of 400 µM. After incubation at room temperature for 3 h, the reaction was quenched with 5 mM l-cysteine and was injected into an SD200 increase 10/300 column equilibrated with SEC buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 2 mM CaCl2 in D2O). Fractions of the monodisperse peak were pooled and equally divided into ten 1.5-ml tubes. The protein was diluted fourfold with SEC buffer. Ligands were added to each tube at a final concentration of 1 mM for naloxone, TRV130, PZM21, MP, buprenorphine, and morphine, 400 µM for DAMGO, 200 µM for lofentanil, and 500 µM for BU72. One tube of protein was kept without ligand. The µOR and ligand were incubated at room temperature for 2 h. Protein in each individual tube was concentrated and split into two parts, one of which was mixed with 20% (v/v) D8-glycerol, transferred to a capillary, and flash frozen. The other part was mixed with a threefold molar excess of Gi1, which was purified in D2O buffer, and incubated for 30 min at room temperature. 1:100 apyrase (v/v, NEB) was added to the G-protein samples to remove free GDP and incubated for 1 h at room temperature. The G-protein samples were then mixed with 20% (v/v) D8-glycerol, transferred to capillaries and flash frozen.

    To make samples in complex with βarr1(∆CT) for DEER studies, µORp∆7(R182C/R276C) was labelled with HO-1427 following a similar protocol above. SEC fractions were pooled and equally divided into 10× 1.5-ml tubes. The protein was diluted fourfold with D2O dilution buffer of 20 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 5 µM C8-PIP2, and respective ligand at a final concentration as indicated above. The µOR was incubated with ligand for 2 h at room temperature. Protein was then concentrated, mixed with a fourfold molar excess of βarr1(∆CT) that was in D2O buffer, and incubated at room temperature for 1 h. The samples were then mixed with 20% (v/v) D8-glycerol, transferred to capillaries and flash frozen.

    Single-molecule FRET experiments and analysis

    All smFRET experiments were performed at 25 °C following previous protocol with some modifications54. In brief, single-molecule FRET studies were performed on a home-built objective-type TIRFM microscope, based on a Nikon Eclipse Ti-E with an EMCCD camera (Andor iXon Ultra 897), and solid-state 532 nm excitation lasers (Coherent Inc. OBIS Smart Lasers). Fluorescence emission from the probes was collected by the microscope and spectrally separated by interference dichroic (T635lpxr, Chroma) and bandpass filters, ET585/65 m (Chroma, Cy3) and ET700/75 m (Chroma, Cy5), in a Dual-View spectral splitter (Photometrics). No bandpass filter was used for Cy7 in the Dual-View spectral splitter. The hardware was controlled and smFRET movies were collected using Cell Vision software (Beijing Coolight Technology).

    The µOR was immobilized on the cover slip via biotinylated M1 Fab and streptavidin. In brief, the assembled glass chamber, which had been cleaned and passivated with biotin-polyethylene glycol, was incubated with 0.05 mg ml−1 streptavidin in 20 mM HEPES 7.5, 100 mM NaCl. One minute later, the unbound streptavidin was washed out by 25 nM biotinylated M1 Fab in incubation buffer (50 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 2 mM CaCl2, 5 mM MgCl2 and 100 µM ligand). The biotinylated M1 Fab was incubated in the channel for one minute and the unbound M1 Fab was washed out by incubation buffer. The N-terminal Flag-tagged, fluorophore-labelled µOR was diluted to around 20 nM in incubation buffer and incubated on ice for 1 h before measurement. The µOR was diluted to about 1 nM and injected into the chamber. The unbound µOR was removed by imaging buffer (incubation buffer + 50 nM protocatechuate-3,4-dioxygenase (PCD), 2.5 mM protocatechuic acid (PCA), 1.5 mM aged Trolox, 1 mM 4-nitrobenzyl alcohol (NBA), 1 mM cyclooctatetraene (COT)). Movies were taken at a frame rate of 10 s−1 using the Cell Vision software. For measurement in complex with GDP-free Gi1, 20 nM µOR in the presence of 100 µM ligand was incubated with 20 µM Gi1 for 30 min followed by addition of 1:100 (v/v, NEB) apyrase. After incubation on ice for 1 h, the complex was diluted and injected into the chamber and measured following the same protocol above. For measurement in the presence of Gi1 and GDP, the surface-immobilized µOR was incubated with imaging buffer, then 20 µM Gi1 and various concentrations of GDP in imaging buffer were injected into the chamber and imaged. For measurement in the presence of βarr1(∆CT), the phosphorylated, Cy3/Cy5-labelled µOR was diluted to about 20 nM in arrestin buffer (50 mM HEPES pH 7.5, 100 mM NaCl, 0.01% LMNG, 0.001% CHS, 2 mM CaCl2, 5 mM MgCl2 and 100 µM ligand, 20 µM C8-PIP2), and 90 µM βarr1(∆CT) was added. After incubation on ice for 1 h, the µOR was diluted to 1 nM in arrestin buffer with βarr1(∆CT) at a final concentration of 90 µM. After immobilization, unbound µOR was washed out with imaging buffer supplemented with 90 µM βarr1(∆CT) and movies were taken.

    To extract the time trajectories of single-molecule fluorescence, collected movies were analysed by a custom-made software program developed as an ImageJ plugin (http://rsb.info.nih.gov/ij). Fluorescence spots were fitted by a 2D Gaussian function within a nine-pixel by nine-pixel area, matching the donor and acceptor spots using a variant of the Hough transform55. The background subtracted total volume of the 2D Gaussian peak was used as raw fluorescence intensity I.

    Actual FRET efficiency was calculated via equation \(E={\left(1+\frac{{I}_{{\rm{D}}}}{{I}_{{\rm{A}}}-\chi {I}_{{\rm{D}}}}\gamma \right)}^{-1}\), where ID is raw fluorescence intensity of donor, IA is raw fluorescence intensity of acceptor, and χ is the cross-talk of the donor emission into the acceptor channel. γ accounts for the differences in quantum yield and detection efficiency between the donor and the acceptor and is calculated as the ratio of change in the acceptor intensity (ΔIA) to change in the donor intensity (ΔID) upon acceptor photobleaching56 (γ = ΔIAID). The χ was 0.05, and the γ was 1 and 0.2 for Cy3/Cy5 and Cy3/Cy7 dye pairs, respectively. FRET traces were picked by a custom-made Matlab script based on three criteria57: (1) signal-to-nose ratio of trances, which is defined as the mean of total intensity before photobleaching divided by its standard deviation, was higher than 4 and 3 for Cy3/Cy5 and Cy3/Cy7 dye pairs, respectively; (2) donor traces have single-step photobleaching; (3) traces last for at least 2 s. To calculate the transition rate in the presence of G protein and GDP, only traces that showed at least one high/low-FRET transition were selected and analysed by a Hidden Markov model-based software (HaMMy)44. Two FRET states were identified by HaMMy. The cumulative frequency count of high-FRET dwell times for each condition was fitted in Origin software to single exponential decay curves, generating high-FRET dwell time. The cumulative frequency count of low-FRET dwell times for each condition was fitted in Origin software to double exponential decay curves and the low-FRET dwell time was calculated as a weighted average accordingly.

    DEER experiments and analysis

    Setup

    Four pulse, Q-band DEER data were collected at 50 K on a Bruker e580 equipped with a QT-II resonator and a 150 W TWT amplifier using the pulse sequence: π/2(νA) – τ1 – π(νA) – (τ1 + t) – π(νB) – (τ2 − t) – π(νA) – τ2 – echo, with τ1 = 300 ns, τ2 = 3.5 μs, Δt = 16 ns, 16-step phase cycling and a repetition time of 510 μs. The observer pulses (νA) were set to 18 ns and 36 ns for π/2 and π pulses, respectively, and applied 70 MHz below resonance. The 100 ns pump pulse (νB) was applied on resonance and consisted of a 50 MHz linear chirp pulse generated by an arbitrary waveform generator. We furthermore used an 8-step ESEEM suppression protocol. All experiments were implemented using Xepr v2.6b.163.

    Analysis

    DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2)58. In brief, all 30 datasets (10× ligand only, 10× ligand + Gi, 10× ligand + β-arr) were analysed simultaneously assuming a variable number of two to seven Gaussians whose mean positions and widths (global fitting parameters) were constrained in the range of 20–100 Å, and 2–20 Å, respectively. For each individual condition the sum of populations (local fitting parameters) was normalized to 1. Each of the thirty datasets was allowed a unique modulation depth (range 0.3–0.7) and each transducer condition allowed for a unique receptor concentration in the range of 25–150 μM. Model-based distance distributions and background corrected dipolar kernels were calculated using DEERlab functions and fit simultaneously to all 30 datasets using the fitparamodel.m routine (Multistart = 10). Post hoc model selection was performed using the Akaike information criterion corrected (AICc) and the more restrictive Bayesian information criterion (BIC) which were both evaluated globally for all DEER datasets and both yielded 6 Gaussians as most parsimonious model. Error analysis using 1,000 bootstrap iterations was performed for all fitting parameters, the dipolar fits and the parametric distance distributions, and evaluated at the 95% confidence level. Significant population changes between different transducer conditions were determined by disjunct 95% confidence intervals and are marked with * (star).

    Comparison of model-based and model-free analysis

    As a control, we also analysed all DEER data using Tikhonov regularization (TR) and model-free based analysis in DEERlab and LongDistances (v.946; http://www.biochemistry.ucla.edu/Faculty/Hubbell/software.html). Regularization or smoothness parameters were determined via AICc and L-curve criterion, respectively. The results from both analyses were superimposable. For comparison, the distance distributions derived from the model-based (6 Gaussian) best fit and model-free DEERlab fits are shown in Extended Data Fig. 5. Both methods yield almost identical distance distributions and reveal all ligand or transducer-dependent distance changes supporting the validity of the model-based fit. Most apparent differences appear in the 35–45-Å distance range, where model-based analysis was able to differentiate two peaks, namely at 39 Å and 43 Å, of different width, namely 3.8 Å and 2 Å. This finding exemplifies one of the inherent advantages of the global, GMM-based fitting approach over Tikhonov regularization or model-free analysis. While Tikhonov regularization or model-free based analyses apply a single regularization or smoothness parameter to the full distance range, the chosen GMM allows different widths for individual distance peaks, as they may exist for different conformational states. Other advantages of the model-based approach include straightforward quantification of each population (Gaussian area) and a rigorous error analysis for each fitting parameter using covariance matrix or bootstrapping based approaches.

    We conducted biological repeats for naloxone and lofentanil with and without G protein. These conditions represent the most distinct ligand/transducer conditions investigated and we observe good reproducibility. In particular, for both ligands, the smaller Gi-induced shifts are accurately reproduced (Extended Data Fig. 8d).

    Radioligand binding

    Membranes of Sf9 cells expressing µOR were used for saturation binding and competition binding. Saturation binding was performed by incubating Sf9 membrane with increasing concentrations of the antagonist [3H]diprenorphine (3H-DPN, Perkin Elmer) for 2 h at room temperature in 0.5 ml of binding buffer containing 50 mM Tris-HCl pH 7.5, 100 mM NaCl, 0.1% BSA. Nonspecific binding of 3H-DPN was measured by adding 10 µM naloxone in the binding reaction. To separate unbound 3H-DPN, binding reactions were rapidly filtered over GF/B Brandel filters. The filters were then washed three times with 5 ml ice-cold binding buffer. Radioactivity was assayed by liquid scintillation counting.

    For competition binding with 3H-DPN, Sf9 cell membrane was incubated with 2.9 nM 3H-DPN and increasing concentrations of DAMGO in 0.5 ml of binding buffer. Binding reactions were incubated for 2 h at room temperature. The free ligand was separated by rapid filtration onto a GF/B Brandel filter with the aid of a 48-well harvester (Brandel). Radioactivity was assayed by liquid scintillation counting.

    The resulting data were analysed using Prism 9.0 (GraphPad Software). The dissociation constant (Kd) of 3H-DPN was calculated by fitting the saturation data in a one-site (total and nonspecific binding) model. The Ki of DAMGO was calculated by fitting the competition binding data in a one-site (fit Ki) model.

    For competition binding with [3H]naloxone, mouse µOR-containing insect cell membranes prepared above were diluted to normalize expression levels between wild-type (1:1,000) and minimal-cysteine mouse µOR (1:100) in 20 mM HEPES pH 7.4, 100 mM NaCl, and 0.05% BSA. Membranes were then incubated with 3 nM [3H]naloxone and serially-diluted orthosteric ligands at their respective final concentrations. Tested ligands were diluted into the buffer above to a final concentration of 100 µM with a fourfold serial dilution series for 10 total concentrations. The only exception is BU72, which was diluted to 1.3 µM final concentration before the same serial dilution. All ligands include independent ‘no ligand’ controls (100% binding) and excess cold naloxone (200 µM) controls (0% binding) to which points were normalized. The mixtures were shaken for 1 h at room temperature before collection onto Filtermat B (Perkin Elmer) and washed with cold binding buffer (20 mM HEPES pH 7.4, 100 mM NaCl). The filters were then dried at 60 °C before adding a sheet of MultiLex B/HS melt-on scintillator sheets (Perkin Elmer) and counts read on a MicroBeta Counter (Perkin Elmer). Quadruplicate data values were plotted and normalized as described above.

    BRET-based assays with TRUPATH and arrestin signalling

    The BRET-based assays were based on TRUPATH59 and arrestin signalling48. To measure µOR’s coupling with Gi1, HEK 293 T cells (ATCC CRL-3216, authenticated by the supplier, routinely tested for mycoplasma) were plated in 10 cm dishes at 3–4 million cells per dish in Dulbecco′s Modified Eagle′s Medium (DMEM) supplemented with 10% FBS. The next day, cell medium was replaced with fresh DMEM + 10% FBS medium. Cells were transfected 2 h later, using a 1:1:1:1 DNA ratio of receptor:Gα-RLuc8:Gβ1:Gγ2-GFP2 (500 ng per construct). Transit 2020 (Mirus Biosciences) was used to complex the DNA at a ratio of 3 µl Transit per µg DNA, in OptiMEM (Gibco-ThermoFisher) at a concentration of 10 ng DNA per µl OptiMEM. The next day, cells were collected from the plate using Versene (0.1 M PBS  +  0.5 mM EDTA, pH 7.4) and plated in poly-d-lysine-coated white, clear-bottom 96-well assay plates (Greiner Bio-One) at a density of 50,000 cells in 200 µl culture medium (DMEM + 1% dialysed FBS) per well. The next day, white backings (Perkin Elmer) were applied to the plate bottoms, and growth medium was carefully aspirated and replaced immediately with 60 µl of assay buffer (1× Hank’s balanced salt solution (1× HBSS, Gibco), 20 mM HEPES, pH 7.4), supplemented with 5 µM (final concentration) coelenterazine 400a (Nanolight Technologies). After a 5 min equilibration period, cells were treated with 30 µl of drug (3×) prepared in assay buffer for an additional 5 min. Plates were then read in an LB940 Mithras plate reader (Berthold Technologies) with 395 nm (RLuc8-coelenterazine 400a) and 510 nm (GFP2) emission filters, at integration times of 1 s per well. Plates were read serially four times, and measurements from the fourth read were used in all analyses. BRET ratios were computed as the ratio of the GFP2 emission to RLuc8 emission.

    To measure coupling of µOR coupling with β-arrestin-1, the procedures are mostly similar to those in BRET-G-protein assays except: HEK 293 T cells were co-transfected in a 1:5 ratio with µOR-Rluc8 and Venus–β-arrestin-1. Before the addition of tested drugs, white backings (Perkin Elmer) were applied to the plate bottoms, and growth medium was carefully aspirated and replaced immediately with 60 µl of assay buffer (1× HBSS, 20 mM HEPES, pH 7.4), supplemented with 5 µM (final concentration in assay buffer) coelenterazine h (Nanolight Technologies). After a 5 min equilibration period, cells were treated with 30 µl of drug (3×) prepared in assay buffer for an additional 5 min. Plates were then read in an LB940 Mithras plate reader (Berthold Technologies) with 485 nm (RLuc8-coelenterazine h) and 530 nm (Venus) emission filters, at integration times of 1 s per well. Plates were read serially four times, and measurements from the fourth read were used in all analyses. BRET ratios were computed as the ratio of the Venus emission to RLuc8 emission. The BRET ratio from G-protein or arrestin assays was plotted using nonlinear regression and Dose-response stimulation equation in Prism 9 (Graphpad).

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Scientists Solve Early Earth RNA Puzzle

    Scientists Solve Early Earth RNA Puzzle

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    Abstract Biochemistry Origin of life Concept

    A groundbreaking study reveals how life’s complexity could originate from simple RNA molecules on early Earth. Through experiments demonstrating RNA’s recycling and replication abilities under specific conditions, such as low salinity and high pH, the research suggests life could emerge from minimal molecular sets in environments akin to volcanic islands. This finding challenges prior assumptions about RNA’s evolution and underscores the potential for life’s emergence in simple, cold prebiotic conditions.

    Recent research illustrates how RNA molecules’ chemical characteristics might have played a crucial role in the development of complex life forms.

    How did complex life manage to evolve on the early, inhospitable Earth? Initially, ribonucleic acid (RNA) must have existed to carry the first genetic information. For these biomolecules to build-up complexity in their sequences, they needed to release water. However, on the early Earth, which was predominantly covered in seawater, this process was challenging.

    In a paper recently published in the Journal of the American Chemical Society (JACS), researchers from the team of LMU professor Dieter Braun have shown that in RNA’s struggle with the surrounding water, its natural recycling capabilities, and the right ambient conditions could have been decisive.

    “The building blocks of RNA release a water molecule for every bond they form in a growing RNA chain,” explains Braun, spokesperson for the Collaborative Research Centre (CRC) Molecular Evolution in Prebiotic Environments and coordinator at the ORIGINS Excellence Cluster. “When, conversely, water is added to an RNA molecule, the RNA building blocks are fed back into the prebiotic pool.”

    This turnover of water works particularly well under low saline conditions with high pH levels. “Our experiments indicate that life could emerge from a very small set of molecules, under conditions such as those prevailing on volcanic islands on the early Earth,” says Adriana Serrão, lead author of the study.

    A New Understanding of RNA Evolution

    Under these conditions, RNA has the ability to split without adding a water molecule. The end of the RNA strand remains water-free and can spontaneously re-form new RNA bonds. Braun’s laboratory demonstrated that the rebinding of this split RNA works efficiently and with remarkable precision when copying the sequence information. This process only takes place when the RNA building blocks are bound to a template RNA molecule with precisely matching base pairs in a double-stranded configuration. This produces a copy of the existing RNA strand before it disintegrates through the addition of water.

    It had previously been assumed that RNA can only copy itself by ‘randomly’ constructing sequences of around 200 nucleotides in length – so-called ribozymes. However, ribozymes can operate only in saline, and thus RNA-hostile, environments. As a result of this new research, these complex ribozyme sequences in the early stages of RNA evolution are not necessary. “The precision is comparable to the copying of RNA achieved by ribozymes,” says Sreekar Wunnava, also lead author of the study. “This means that an RNA world could arise without the prior necessity for long complex sequences.”

    Early life thus consisted of a very simple metabolic process whereby RNA sequences were copied by means of continuous replacement with recycled molecules. All that is needed for this to happen is an alkaline freshwater environment such as still exist today on volcanic islands like the Hawaiian archipelago or Iceland. “And so life could have emerged from a simple, cold prebiotic primordial soup of RNA building blocks,” explains Braun. Although the reactions take place very slowly under these conditions and require several days to complete, there was no shortage of time at the start of evolution and the cold freshwater refuges on primeval volcanic islands allowed RNA to survive on the otherwise inhospitable early Earth.

    Reference: “High-Fidelity RNA Copying via 2′,3′-Cyclic Phosphate Ligation” by Adriana Calaça Serrão, Sreekar Wunnava, Avinash V. Dass, Lennard Ufer, Philipp Schwintek, Christof B. Mast and Dieter Braun, 19 March 2024, Journal of the American Chemical Society.
    DOI: 10.1021/jacs.3c10813



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