Category Archives: Cell Biology

New analysis of cancer cells identifies 370 targets for smarter, personalized treatments – News-Medical.Net

January 19, 2024 medical

A new, systematic analysis of cancer cells identifies 370 candidate priority drug targets across 27 cancer types, including breast, lung and ovarian cancers.

By looking at multiple layers of functional and genomic information, researchers were able to create an unbiased, panoramic view of what enables cancer cells to grow and survive. They identify new opportunities for cancer therapies in a significant leap towards a new generation of smarter, more effective cancer treatments.

In the most comprehensive study of its kind, researchers from the Wellcome Sanger Institute, Open Targets and their collaborators, pooled together data from 930 cancer cell lines. They then used machine learning methods to find the drug targets that show the most promise for developing new treatments, and the patients who would most benefit from such treatments. This involved assessing the occurrence of these targets in actual patient tumors and linking them to specific biological markers and genetic and molecular features found in the tumors.

The findings, published today (11 January) inCancer Cell, not only bring researchers one step closer to producing a full Cancer Dependency Map1of every vulnerability in every type of cancer, but help guide focused efforts to accelerate the development of targeted cancer treatments.

There are many types of cancer that currently lack effective treatments, such as liver and ovarian cancers. Chemotherapy and radiotherapy are effective treatments, but unable to distinguish normal cells from cancerous ones, so can cause damage throughout the entire body with harsh side effects, such as extreme fatigue, nausea and hair loss.

New precision drugs based on the exact genetic mutations that drive the cancer are needed to help the millions of patients diagnosed with some form of cancer each year, responsible for one in six deaths worldwide2. However, drug development has a 90 per cent failure rate3, making it both costly and inefficient.

With over 20,000 potential anti-cancer targets in the genome, determining which are suitable to target for specific types of cancers and patients is a significant challenge.

In this new study, researchers from the Wellcome Sanger Institute and their collaborators set out to narrow down potential drug targets. By analyzing data available from the Cancer Dependency Map project, which involved CRISPR technology4to disrupt every gene inside 930 human cancer lines one at a time, they were able to produce the most comprehensive view of potential new cancer targets to date.

The researchers first identified weaknesses within different cancer types so-called genetic dependencies, meaning which genes, proteins or cellular processes that cancer cells rely on to survive that could be harnessed to make new therapies. They then linked those weaknesses to clinical markers to identify patients in which those therapies would be most effective. Finally, they explored how dependency-marker pairs fit into known networks of molecular interactions within cells, providing clues as to how cell biology is disrupted by cancer, and which targets might yield the most effective therapies.

The work provides a clearer understanding of which types of cancer can possibly be treated by existing drug discovery strategies and pinpoint areas where novel and innovative approaches are needed.

The findings underscore the importance of tailoring treatments to the unique characteristics of each cancer, promising more personalized care for patients with fewer side effects in the future.

Dr Francesco Iorio, co-lead author of the study from the Computational Biology Research Centre of Human Technopole, said: Analyzing the largest-ever cancer dependency dataset, we present the most comprehensive map yet of human cancers' vulnerabilities - their "Achilles heel". We identify a new list of top-priority targets for potential treatments, along with clues about which patients might benefit the most - all made possible through the design and use of innovative computational and machine intelligence methodologies.

Dr Mathew Garnett, co-lead author of the study at the Wellcome Sanger Institute and Open Targets, said: Our work uncovers 370 candidate priority targets for tackling the most prevalent cancers, including breast, lung and colon cancers. This work exploits the latest in genomics and computational biology to understand how we can best target cancer cells. This will help drug developers focus their efforts on the highest value targets to bring new medicines to patients more quickly.

Two people might have the same type of cancer, but their diseases can behave differently. That is why we need precision medicine. This ambitious work is a compelling example of research informing drug discovery from the start, paving the way for more effective precision cancer therapies. Giving people treatments for their unique cancer can improve the odds of success and help more people affected by cancer live longer, better lives.

Dr Marianne Baker, Science Engagement Manager, Cancer Research UK

Source:

Journal reference:

Pacini, C., et al. (2024). A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell. doi.org/10.1016/j.ccell.2023.12.016.

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New analysis of cancer cells identifies 370 targets for smarter, personalized treatments - News-Medical.Net

Cell Biology

EU funding for pioneering research on the treatment of gliomas – EurekAlert

January 19, 2024 medical

image:

Dr. Anne Rgnier-Vigouroux, coordinator of the GlioLighT project funded by the EU

Credit: : Anne Rgnier-Vigouroux

Gliomas are the most common type of brain tumors and are difficult to treat because they tend to be diffuse and are often located deep within the brain. A very promising and innovative technique for the therapy of gliomas will be investigated in a new EU project and should pave the way for clinical use. "We are convinced that our research will help to significantly improve the treatment of gliomas," said the coordinator of the project, Dr. Anne Rgnier-Vigouroux of Johannes Gutenberg University Mainz (JGU). The other project partners are Aston University in Birmingham, the University of Barcelona, LMU Munich, the Finnish laser manufacturer Modulight, and MODUS Research and Innovation Ltd., a British not-for-profit organization providing advice to secure research funding. Through its Pathfinder program, the European Innovation Council (EIC) supports the exploration of highly innovative and speculative new technologies at the very earliest phase of their development. The new GlioLighT project will receive funding of approximately EUR 2.2 million over the next three years.

Using extremely toxic singlet oxygen to combat gliomas

Gliomas are an extremely deadly form of cancer, mainly due to the inaccessibility of the brain and the widespread dissemination of the tumor cells. These diffuse cells are often anchored too deeply in the brain to be removed completely using current therapeutic techniques, such as resection, irradiation, or chemotherapy. A promising alternative to eliminate glioma cells is to generate reactive oxygen species (ROS) directly at the affected sites. Currently, ROS can only be produced through photodynamic therapy, which has been employed for decades but involves potentially toxic photosensitizers. To avoid detrimental side effects of this type of treatment, the GlioLighT partners propose a different approach involving direct light therapy. This method will entail the direct generation of ROS using laser light at a wavelength of 1,267 nanometers, which is in the near-infrared range. The irradiation of light will produce singlet oxygen, which destroys cancer cells.

"Assuming we can reach the tumor cells directly with laser light, we will no longer need photosensitizers as amplifiers. We will have a minimally invasive and selective procedure, representing a completely new strategy for glioma treatment," said Dr. Anne Rgnier-Vigouroux. The benefits of this approach include improved efficacy of treatment, earlier intervention, and reduced costs. Currently, the precise cancer-inhibiting mechanism of direct light therapy and the safety of the procedure itself are not well understood.

Janus-headed macrophages: Anti-inflammatory potential promotes tumor growth

The laser radiation will also affect the cells of the immune system which are expected to fight tumor cells. "We will be paying special attention to the effects on tumor cells but also on immune cells, particularly the macrophages," emphasized Rgnier-Vigouroux. Macrophages are scavenger cells that take up pathogens and render them harmless. They can trigger inflammatory reactions and thus contribute to the bodys immune response and the fight against tumor cells. However, they can also have anti-inflammatory activities and thus prevent other immune cells from attacking the tumor.

"Macrophages can kill tumor cells, but they can also be recruited and manipulated by them, resulting in tumor growth." It is this second possibility that Dr. Anne Rgnier-Vigouroux aims to prevent: "We need to eliminate the tumor cells and, at the same time, trigger the immune cells in their vicinity to exert toxic effects on them."

Other aspects that the GlioLighT researchers will investigate include the type of tumor cell death induced by the laser light, the effect of laser light exposure on healthy cells in the brain, such as the neurons, and the determination of a safe dosage that can be administered without harming healthy cells. The project partners will work on innovative ultrashort pulse lasers to optimize the optical penetration through tissue and to minimize potential risks, ensuring that direct light therapy is suitable for clinical application. Ultimately, the development of a preclinical GlioLighT delivery and sensing system (pcGlio-DSS) should improve glioma treatment.

EIC Pathfinder program provides support for visionary and high-risk technologies in their early stages of development

The GlioLighT Next Generation Glioma Treatments using Direct Light Therapy project is being financed through the future-oriented EIC Pathfinder program under the aegis of the European Union's Horizon Europe program. The EU will provide a total of EUR 2.2 million in financial support to the project, of which approximately EUR 770,000 is earmarked for the research to be undertaken at Johannes Gutenberg University Mainz. The purpose of the EIC Pathfinder Open program of the European Innovation Council is to identify radically new technologies that have the potential to create new markets. Grants are thus awarded to groundbreaking and highly speculative projects that are still in an early stage of development. The participants in a project that has been awarded EIC Pathfinder support are typically visionary and entrepreneurial-minded specialists and researchers at universities, research institutes, start-ups, high-tech SMEs, and individuals working in the industrial realm who have a particular interest in investigating and creating technological innovations.

Project coordinator Dr. Anne Rgnier-Vigouroux has been research group leader at JGU's Institute of Developmental Biology and Neurobiology since January 2013. Her group focuses on the study of cerebral anti-tumor immunity, particularly on the role of microglia and macrophages in brain tumor biology.

The future of mRNA biology and AI convergence – Drug Target Review

December 22, 2023 medical

In the rapidly evolving landscape of mRNA biology and artificial intelligence (AI), Anima Biotech stands at the forefront, a unique approach that reshapes our understanding of diseases and transforms the drug discovery process. mRNA biology holds immense potential with RNAi drugs in the market and mRNA vaccines showing promise, particularly in cancer trials.

mRNA biology has become widely recognised as a new drug mine. RNAi drugs are in the market and mRNA vaccines, initially developed for Covid-19, are already in trials for cancer. mRNA is an intermediate form that codes the instructions for making proteins but the biology around its regulation is not well understood and is still an uncracked code. Complex regulatory pathways move mRNAs through their life cycles and control when, where, and how much of each individual protein is made. Examples of such mechanisms include RBPs, splicing, and relocalisation of mRNAs among many others. All of these are novel targets to be explored in small molecule drug discovery as well as for improving the efficacy of mRNA vaccines.

However, our inability to understand that biology is what stands in the way of further progress. And really, this comes to the biggest question of all: what is the underlying mechanism of a given disease? There must be something different in those diseased cells, some dysregulated pathway that is causing that disease phenotype. And since mRNA biology is like the highway of cell biology, it would likely be visible there. And this is exactly where AI can provide us with an extremely powerful new strategy. The elucidation, or decoding of complex biology is becoming possible with AIs ability to process large amounts of visual data and recognize patterns in images. We could use it to identify the underlying disease mechanism, the dysregulated pathway, or as we call it at Anima, the disease signature.

AI has become very capable in both understanding and generating images. So, you could capture images of live mRNA biology from millions of diseased and healthy cells and have an AI neural network trained to recognize the differences. It would see in the images the pathway that tells the diseased cells apart from the healthy ones, essentially elucidating the disease mechanism. You could then use large language models to research all available knowledge around that pathway and novel drug targets. This is where I see things going: using AI to process vast amounts of data coming through images taken from live biology of a disease model, identify what is causing the disease, the disease signature, and then have AI suggest targets along that pathway. You can then screen molecule libraries to find modulators of the underlying disease biology. So, as you can see, AI has the potential to completely transform the process by visualising and decoding disease biology.

What we are doing at Anima is applying our proprietary mRNA biology visualization technologies to generate millions of images of mRNA regulatory pathways in disease models. We feed them to our AI mRNA Image Neural Network which has been trained on over 2 billion such images, proprietary data that came from our projects across a decade. We pinpoint the disease mechanism and then apply our mRNA knowledge graph and our mRNA biology LLM, an augmented large language model that we built around mRNA knowledge. This elucidation of underlying disease biology is now happening at the beginning of a drug discovery process, rather than being tried at the end of it. For a very long time, companies had to choose between two very different approaches to drug discovery. The first approach is to come up with a proposed target and screen against that protein. The problem here is that you are betting on the target to have an effect on disease phenotype.

When drugs fail in the clinic it is mainly because we do not understand the disease mechanism. The target that was chosen is the secret switch to turn off the disease phenotype. So, this betting on a target is a real problem and for many diseases, we are still in the dark with regards to what is the real cause and do not understand the full biology. An alternative approach is phenotypic screening and there, you basically screen many molecules and look for those that reverse the phenotype. You dont know the target and you are not betting on one. So, this approach has the advantage of being completely unbiased but its big downside is that you dont have any prior understanding of the biology and even after you find some active molecules, it is still very hard to elucidate their mechanism of action and molecular targets.

Our mRNA Lightning platform represents a game changer that solves this fundamental problem. It retains the advantages of phenotypic screening, but it starts with an elucidation of disease biology. The process starts with an analysis of the disease in our mRNA knowledge graph, suggesting pathways that are most relevant to the disease. These become proposed disease mechanisms, pathways regulating mRNA biology. We then go into actual cells that are disease models. We take millions of images in a fully automated lab, and we give them as training data to our AI image neural network. It looks at the images of these proposed pathways and finds the one that shows the biggest change. That becomes the pathway that we will be targeting.

Notice that it will give you this pathway not from a couple of publications and a handful of experiments It will look at the results of millions of single-cell experiments running in parallel and will recognize the common pattern. It will suggest a few alternatives by ranking possible pathways. Now we have a specific biological pathway to go after. We proceed with drug discovery by screening against actual disease mechanisms, the disease signature. We are looking for molecules that modify the image of the pathway in diseased cells, so they now look like the images coming from healthy cells. We are seeing that the exact pathway that we confirmed is the difference, so we have a preconceived notion of the MOA already. The molecules that are coming out of the screen as active compounds now can go through a rapid iterative process where biologists work with other major elements of the AI in our platform, namely the mRNA biology LLM and the Lightning copilot working together in our MOAi technology. It looks at the results and suggests confirmatory experiments to identify the molecular targets of compounds and this typically converges at a fraction of the time that it typically takes to identify the MOA of drugs. It is because we have identified the disease mechanism at the beginning of the process rather than attempting to elucidate it in the end.

The technology that got us started was the ability to visualize the translation of mRNAs into proteins. This was TranslationLight, enabling for the first time to see in images where, when, and how much of the mRNA is translated. In version 1.0 we were leveraging this technology in order to identify molecules that modulate mRNA translation. We would basically look for compounds that affect the light. Molecules that were decreasing the light were inhibiting translation. If they increase the light, they increase translation. It was a very different readout that enabled us to run what seemed like a phenotypic screening but instead of being run in the dark it was running in the light because we were watching an actual major biological process, the translation of mRNAs. We are talking 2013 here so that was a decade ago. Since then, we have generated over a billion of such images from many disease models and cell types. We also built our first automated mRNA biology lab that was running those millions of experiments in cells and taking all these images, uploading them to the cloud for analysis. We built a lot of computational biology stuff to analyse them in order to identify active ones.

Our lead program in lung fibrosis emerged as a strong showcase of how all this worked. All this encouraged us to develop additional visualisation technologies and led to TranscriptLight, over a dozen visualisation technologies that enabled us to see mRNA transcripts, being it single or multiple. Now with this, we have expanded our capabilities to identify compounds that affect anything along the life cycle of mRNA, mRNA biology modulators. We would screen our libraries and see in images the effect of hit molecules on the mRNA, across its life cycle. By doing that we again expanded our image dataset, reaching nearly 2 billion images, the worlds largest dataset of mRNA biology visualisations. We also developed dozens of MOA assays that basically were the other side of these visualisations, experiments that would enable us to look at what a compound is doing and compare it to those known behaviours.

We expanded our automation in our tera-scale lab. Consider that all this was in 2020 and AI was still on the side lines as an experiment. But it became clear to us that we are generating a very large dataset of mRNA biology images, the only one in the world in fact and that has tremendous value. We started to consider how we could use AI to make sense of that data. In Lightning 2.0, we introduced MOAi, a technology that used AI to elucidate the mechanism of action of AI. In Lightning 2.0, we introduced MOAi, a technology that used AI to elucidate the mechanism of action of AI. The idea was that AI would take all evidence collected from the screen and advise biologists on the sequence of experiments to rapidly discover how compounds work. This was a big thing because we leveraged AI to tackle the biggest problem of phenotypic screening, to identify the MOA of hit compounds.

We would actually start doing that very early, right after the hit stage of the screening and it would run along the project by leveraging all the data. But it wasnt until 18 months ago that the full power of AI could be brought into the platform in a way that is a game changer. And that came in Lightning 3.0 with the development of PathwayLight, the ability to visualise mRNA biology regulatory pathways. That was a turning point in the development of the platform. We could for the first time visualise disease mechanisms. We turned back to the dataset of the images we had, and we saw that for a given disease, we could train a neural network on images of diseased and healthy cells, use PathwayLight to see all different pathways, and identify the one that is dysregulated in the disease. The AI image network proved to be very effective in identifying these disease signatures. We then looked back at the vast amount of knowledge that we have collected over a decade around mRNA biology.

We decided to create the worlds first mRNA biology knowledge graph and apply LLM technology to interact with it. Earlier this year we incorporated into the platform the first generation of these technologies. So, with Lightning 3.0 what you are basically seeing is AI everywhere: there is AI on the imaging side, used to identify disease signatures, enabling us to screen against disease-relevant biological pathways. Then there is the mRNA knowledge graph and the LLM that is used to query that, and the Lightning co-pilot is a chat interface across the whole system that biologists can use to interact with all that data. It all comes together in Lightning 3.0 in the form of three applications: target discovery, drug discovery, and MOA discovery. Going forward we will continue to expand and integrate these three major applications.

Understanding the mechanism of action of drugs is a decades-long problem. There are many drugs for which the MOA has never been elucidated. Some companies have recently applied high-scale imaging and AI analysis technologies. They are looking for compounds that modify the image of diseased cells to look more like the healthy ones, but you are left with that problem. This seems much like what we have been talking about here but in fact, is different in a fundamental way. Cell morphology images do not provide any immediate biological meaning in the context of disease biology. What we are doing is very different. We are visualising the pathways in mRNA biology and not cell morphology. We identify the disease signature and find compounds that directly modulate that pathway. So, when we turn to the challenge of figuring out how these compounds are working, their MOAs, we already have a pretty good understanding and an inherent rationale for that. If they show that then the image of the disease signature is modified from an image of diseased cells to healthy ones, and the mechanism of action and molecular targets must be around that pathway.

This fact is changing the game because now we have a pathway that we can study. You have a compound, and you have a hypothesis for the pathway it is working through. What you really need now is to figure out which experiments you must run to quickly understand which protein along the pathway could be the molecular target. You have a much small number of candidates, but it could still be hundreds of experiments in trial and error. They generate a lot of information and understanding all of these results and figuring out what to do next is still a big challenge. So, this is the 3rd place where AI is applied in the platform. MOAi takes the pathway and goes back to the knowledge graph. It then devises a strategy that is basically to decide on the sequence of experiments to conduct in order to move the MOA elucidation process forward. At each step, an experiment is conducted, and the results are brought back into the system. The information is brought back into the knowledge graph, expanding the original knowledge with actual results. This in turn makes the model look at all the data and suggest the next experiment. But doesnt do this by itself or alone. We figured that LLMs are still highly experimental when it comes to their understanding of biology, so we created an architecture where MOAi is working through the Lightning co-pilot in a biologist-in-the-loop model. This means that biologists can ask the system for its suggestions for targets and experiments but can also suggest their own ideas and the system will evaluate them. The different MOAs are continuously ranked by considering the results from all experiments and you have like a compass visually gravitating to the north, which is the MOA. This is an iterative, creative process where biologists work with AI to elucidate the underlying mechanism of action but at the same time, it increases our understanding of the biology of the disease.

Our partnerships with Lilly, Takeda, and AbbVie, coupled with our 20 active drug discovery programs indeed validate our technology and our overall approach in mRNA biology drug discovery. What these companies have recognized right away is that our unique ability to visualize mRNA biology gives them a whole new strategy in the discovery of drugs against many hard targets. You are actually seeing the disease mechanisms and how they play out to differentiate healthy and diseased cells. They then realised that we are also capable to analyse all that information and decode this mRNA biology. It is a fundamentally different approach from the ones that all the other players in small molecule mRNA drugs are taking. No one can visualise and decode mRNA biology. We are also the only company in the space that has built a platform that brings AI to mRNA biology.

I like to call what we do mRNA biology AI and I believe that this approach can significantly impact the entire landscape of mRNA biology research and applications. It provides major new insights into underlying disease biology. AI neural networks that recognize disease mechanisms and can identify the pathway that has become dysregulated, the disease signature is a huge step forward in our understanding of what is causing a given disease. This has the power to us discover treatments for so many diseases. This is the most important thing. Regarding applications, our platform really covers the whole process from target discovery, through drug discovery to MOA discovery, bringing the superpower of AI to transform the drug discovery process. There are companies out there using AI to analyse publications. Thats computational and it is valuable, but I believe that the ability to visualize actual mRNA biology in disease cells and to be able to decode that biology is key because you are now bringing all this experimental data from the real world and again you are using the power of AI LLM with a knowledge graph to make sense of all of that data.

We were talking mostly about small molecule drugs but really the approach and the platform are applicable across any research into mRNA biology. We are seeing now mRNA vaccine companies coming to us as they want to use these capabilities to better understand the biology around synthetic mRNAs and how they interact with the regulatory mechanisms. By understanding this they can improve the efficacy of those vaccines. So, its really all about that fundamental thing, bringing the superpower of AI to mRNA biology, visualising disease mechanism and really decoding that underlying biology. It is applicable to the discovery of novel targets and new drugs and to the understanding of how they work. We are just in the beginning of using AI across computational and experimental and there is tremendous potential here. I think that this is going to be transformational to our understanding of diseases and to the development of new treatments.

About the author

Yochi Slonim

Co-Founder & CEO, Anima Biotech

A serial entrepreneur in software and biotech, Yochi Slonim has built multiple companies as a founder and CEO through all phases of growth all the way to IPOs and large M&A exits. As a Co-founder and CEO of Anima Biotech, he is driving the companys strategy and business development at the intersection of mRNA biology and AI.

Prior to Anima, Yochi was a co-founder of Mercury Interactive. As CTO and VP R&D from the companys early days, he created product vision and strategy and led a multi-product organisation of 200 developers.

In 2000, Yochi was founder and CEO of Identify. Yochi founded ffwd.me, a unique startup acceleration program where he led a team that worked with over 25 startups in diverse areas and technologies, developing strategy, products and go to market operations while raising multiple rounds of financing from VCs and private investors.

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The future of mRNA biology and AI convergence - Drug Target Review

Cell Biology

The future of artificial breast milk, according to one lab – Quartz

December 22, 2023 medical

Some 15 or so years ago, Leila Strickland was mother to a newborn struggling to breastfeed her weeks-old son, and quickly realized how difficult breastfeeding can be. Today, Strickland and her biotech company, Biomilq, are working to make the benefits of breast milk more accessible to mothers and their infantsby engineering it in a lab.

Breast milk has long been considered the highest standard for infant nutrition, linked to a myriad of benefits for both babies and nursing parents. Yet it remains difficult or unfeasible for many to breastfeed: at six months old, less than a quarter of US infants are breastfeeding as recommended.

When she gave birth to her son, Strickland was completing postdoc research in cell biology at Stanford University, and decided to focus on mammary cells, where little work had been done. By 2020, she hit a first-of-its-kind breakthrough: lab-grown mammary cells that were able to produce some of the nutrients found in human milk. Together with food scientist Michelle Egger she co-founded Durham, N.C.-based Biomilq to develop and scale the discovery.

Stricklands advance was made possible with the help of hundreds of volunteers who donated samples of their breast milk. She also landed $3.5 million in seed money from Breakthrough Energy Ventures, an investment firm founded by Bill Gates to fund innovations focused on climate change. It was a natural fit, given that infant formula made from cow milk is hardly sustainable: it uses an enormous amount of water and produces up to 5,700 metric tons of CO2 each year to feed just one baby.

Investment interest in Biomilq ratcheted up in 2022 when infant formula brands were recalled over safety concerns, sparking worldwide shortages. For her part, Strickland cautions that fully lab-grown milk will not be able to replace baby formula anytime soon. She and Biomilq have recently moved away from her original goal of creating milk individually tailored to one persons breast cells. Thats unrealistic because milk is incredibly complex, she says; its complex enough that there isnt even an agreed-on definition for it. Were now focused on developing certain key nutrients found in breast milk.

Biomilq plans to partner with manufacturers that can include its nutrients in their formula products, and Strickland says she expects to make a major announcement early in 2024. Shes hopeful, she adds, that Biomilq will have its first commercial product in three to five years.

This story is part of Quartzs Innovators List 2023, a series that spotlights the people deploying bold technologies and reimagining the way we do business for good across the globe. Find the full list here.

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The future of artificial breast milk, according to one lab - Quartz

Cell Biology

Shedding new light on the hidden organization of the cytoplasm – News-Medical.Net

December 22, 2023 medical

Back in 2018, the lab of Christine Mayr, MD, PhD, at Memorial Sloan Kettering Cancer Center (MSK) introduced the world to a key cellular component that had been hiding in plain sight.

Now the lab is back with important results that build on that discovery. New findings published in Molecular Cell provide details about the hidden organization of the cytoplasm -; the soup of liquid, organelles, proteins, and other molecules inside a cell. The research shows it makes a big difference where in that cellular broth that messenger RNA (mRNA) get translated into proteins.

"You know the old real estate saying, 'location, location, location.' It turns out it applies to how proteins get made inside of cells, too," says Dr. Mayr, a molecular and cell biologist at the Sloan Kettering Institute, a hub for basic and translational research within MSK. "If it's translated over here, you get twice as much protein as if it's translated over there."

This first-of-its-kind study highlights the degree to which the cytoplasm is "beautifully organized," rather than being just a big jumble of stuff, she says.

Not only do the findings shed new light on fundamental cellular biology, but the knowledge also holds promise for increasing or altering the production of proteins in mRNA vaccines and therapies, the researchers note.

The study was led by former lab member Ellen Horste, PhD, whom Mayr tapped for the daunting but exciting project when she joined the lab several years ago. Dr. Horste received her doctorate from the Gerstner Sloan Kettering Graduate School in June and now works for a gene therapy company.

When we started, we had a hard time getting funding for this project. Everyone thought isolating the individual components would be totally impossible. This was really Ellen's project from her first day in the lab to her last day. It was quite challenging, and I couldn't be more proud of her."

Dr. Christine Mayr, MD, PhD, at Memorial Sloan Kettering Cancer Center

Adapting an approach commonly used by immunologists, the team was able to color-code individual particles within cells using antibodies and then sort them by color. They used RNA sequencing to identify which RNAs were associated with which particles.

"And it was really striking to see that in each of these intracellular neighborhoods, very different types of mRNAs were being translated," Dr. Mayr says.

Most of the well-known components inside a cell have a defined shape and come wrapped in an exterior membrane: the nucleus, mitochondria, lysosomes, the Golgi apparatus.

Two of the key components at the heart of the Mayr team's study don't have membranes -; which is what has made them so hard to find in the first place, and a challenge to isolate and study in the lab.

A quick biology review: Cells build proteins using instructions encoded in DNA. Those DNA sequences are transcribed into mRNA inside the cell nucleus. These messenger RNA then move out into the cytoplasm where they are translated into a useful protein.

The new study demonstrated that where in the cytoplasm this translation step happens isn't random, and that there's an underlying logic or "code" that directs mRNAs to specific neighborhoods within the cell.

"The whole cytoplasm is nicely compartmentalized," Dr. Mayr says. "We were able to demonstrate there is a code at work that's based on the mRNA's biophysical features -; their size and shape -; and the particular RNA-binding proteins they partner with. This code directs the mRNAs to different locations for translation."

Through a painstaking series of experiments, the research team was able to show that mRNAs of different lengths and shapes tend to gravitate to specific neighborhoods. And that if you intervene to redirect them to a different location, it can have a profound impact on the amount of protein that gets produced and on the protein's function.

The researchers looked at mRNAs that locate to the surface of the endoplasmic reticulum (an organelle involved in protein synthesis and other cellular functions). It's well established that proteins associated with cellular membranes and those that get secreted by the cell for use elsewhere are translated there. The research revealed that nearly 15% of mRNAs that encode non-membrane proteins are also translated at the ER -; and they encode large and highly expressed proteins.

Meanwhile, the mRNAs that get translated in the cytosol (the liquid part of the cytoplasm) tend to be very small proteins.

And mRNAs that locate to TIS granules tend to be transcription factors (proteins that regulate the transcription of genes). TIS granules are a membrane-less cellular component Mayr's lab discovered in 2018. They form a network of interconnected proteins and mRNAs, and are closely allied with the endoplasmic reticulum, forming a distinct space where mRNA and proteins can collect and interact.

A fluorescent microscopy image of a cell, with TIS granules shown in red and the endoplasmic reticulum is shown in green. The central black area is the cell's nucleus.

Cracking the code for how mRNA localize to different locations revealed some surprising findings.

After discovering the TIS granule network five years ago, the lab had turned its attention to understanding which of the many thousands of mRNAs in a cell localize there, and whether they have shared characteristics.

The team homed in on one part of the mRNA that doesn't usually get much attention -; the tail. It's separate from the middle part of the mRNA, which contains the instructions for building the protein. Scientists call the tail the three prime untranslated region (3 UTR), and it turns out to be critical for the localization process.

"The tail usually contains a longer sequence than the part of the RNA that's actually used to make the protein," Dr. Mayr says. "But for a long time, people didn't pay that much attention to the tail regions since you can still make the protein without them." (They're also important in other ways, as Dr. Mayr outlined in a 2019 review article.)

It turns out that the tail is essential for partnering with RNA-binding proteins so that, together, the mRNA goes to the correct translation region within the cell. (RNA-binding proteins are a type of protein that attaches to RNA molecules and can modulate various aspects of their activity.)

At first the team thought it was primarily these RNA-binding proteins that directed the action -; guiding the mRNAs to neighborhood one, neighborhood two, and so forth, Dr. Mayr says.

"But the really surprising finding was that the RNA-binding proteins actually play a secondary role rather than a primary role in the process," she says.

The default sorting of mRNA to a location, the researchers found, is based on the overall size and shape of the mRNAs. But being in partnership with a binding protein can override this default and redirect them.

"Our data show that if you translate an mRNA in the TIS granules, the resulting protein will perform one function, and if you translate it outside of the TIS granules, it will perform a different function," she says. "And this is how, in higher organisms like us, one protein can have more than one function."

One specific protein the team examined during the study is MYC. The MYC gene is one of the more famous oncogenes, and mutations in MYC underlie the development of many cancers.

"We observed that several MYC protein complexes were only formed when MYC mRNA was translated in the granules and not when it was translated in the cytosol," Dr. Mayr says. "Our results show there's an important biological relevance to these neighborhoods, even when only about 20% of mRNAs get translated in the TIS granules."

Together, these insights suggest that mRNA could be targeted to achieve different functions, as well as to vary the amount of a protein that gets produced, she adds.

"So, we hope that in the future we can make smarter medicines by making more or less of a particular factor, and also by manipulating its function," Dr. Mayr says. "This probably won't happen in the next five years, but it's something we are paving the way to do."

Source:

Journal reference:

Horste, E. L., et al. (2023) Subcytoplasmic location of translation controls protein output. Molecular Cell. doi.org/10.1016/j.molcel.2023.11.025.

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Shedding new light on the hidden organization of the cytoplasm - News-Medical.Net

Cell Biology

Bugs that help bugs: How environmental microbes boost fruit fly reproduction – EurekAlert

December 22, 2023 medical

image:

Drosophila oogenesis process

Credit: Osaka university

Osaka, Japan For many of us, when we think of microbiomes, our first thoughts are probably about the beneficial microorganisms that live in our guts. But now, researchers from Japan and US have discovered how the microbes living in fruit flies can enhance their reproduction.

In a recently published study in Communications Biology, the research group has revealed that microbes in the fruit fly microbiome are involved in controlling the germline stem cells that form eggs, as well as subsequent egg maturation, in female fruit flies.

The microbiomethe community of microorganisms that live together in and on a hosthas a huge role in facilitating functions that are necessary for survival. This includes metabolic regulation, intake of nutrients, and reproduction, including improving fertility in conditions of inadequate nutrition. However, the specific molecular mechanisms enabling microbes to control the maturation of the germline (the lineage of cells within an organism that gives rise to eggs and sperms) are still a mystery.

We propose that microbes control various stages of oogenesis, which leads the production of eggs in the ovary, says lead author of the study Ritsuko Suyama. They may do this by modifying hormone levels and their successive pathways and can therefore promote host fertility in conditions of poor nutrition.

The researchers investigated the effects of microbes on oogenesis in fruit flies, Drosophila melanogaster. Using genetic analysis, the team revealed that microbes boost oogenesis by both accelerating the division of ovarian cells and suppressing programmed cell death, as well as increasing the production of germline stem cells (GSC) by enhanced cell division and eventually increasing the number of mature eggs in females.

We discovered that the microbes enhancement of reproductive function was controlled by the activation of the hormonal pathways for ecdysone and juvenile hormones in the germline stem cellsthe cells that develop into eggs, explains Toshie Kai, senior author.

Ecdysone is a steroid hormone that regulates moulting in insects. The researchers found that the ecdysone pathway may be an important mediator for a microbe-induced increase of GSCs and egg maturation. Meanwhile, the juvenile hormone pathway is involved only in GSC proliferation, which indicates that the hormonal pathways are activated during different stages of oogenesis.

Our results show that environmental microbes can improve host reproduction by controlling hormone release and increasing oogenesis in their hosts, says Suyama.

These new discoveries about host-microbe interactions could present new avenues for improvements in reproductive health, for example via new treatments involving probiotics. Specifically, the findings from this study will contribute to the understanding of how microbes boost their hosts reproductive processes, which will open the door for new methods to improve fertility and infertility treatment.

###

The article, Microbes control Drosophila germline stem cell increase and egg maturation through hormonal pathways, was published in Communications Biology at DOI:10.1038/s42003-023-05660-x

About Osaka University

Osaka University was founded in 1931 as one of the seven imperial universities of Japan and is now one of Japan's leading comprehensive universities with a broad disciplinary spectrum. This strength is coupled with a singular drive for innovation that extends throughout the scientific process, from fundamental research to the creation of applied technology with positive economic impacts. Its commitment to innovation has been recognized in Japan and around the world, being named Japan's most innovative university in 2015 (Reuters 2015 Top 100) and one of the most innovative institutions in the world in 2017 (Innovative Universities and the Nature Index Innovation 2017). Now, Osaka University is leveraging its role as a Designated National University Corporation selected by the Ministry of Education, Culture, Sports, Science and Technology to contribute to innovation for human welfare, sustainable development of society, and social transformation.

Website: https://resou.osaka-u.ac.jp/en

Communications Biology

Experimental study

Animals

Microbes control Drosophila germline stem cell increase and egg maturation through hormonal pathways

21-Dec-2023

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Bugs that help bugs: How environmental microbes boost fruit fly reproduction - EurekAlert

Cell Biology

Cells Move in Groups Differently Than They Do When Alone – NYU Langone Health

December 14, 2023 medical

A protein that in single cells helps generate the force needed to move works differently in cells moving in groups, a new study shows.

Cells push and pull on each other and the surrounding tissue to move as they form organs in an embryo, heal wounds, track down invading bacteria, and even become cancerous and spread. Led by researchers at NYU Grossman School of Medicine, the new study examined how forces are generated by a group of 140 cells called the primordium that adhere to each other as they move into place in zebrafish embryos. Zebrafish are a major model in the study of development because they are transparent and share cellular mechanisms with humans.

Published online December 13 in Current Biology, the new work reveals how the cells in the primordium use a protein called RhoA to trigger forces that move the group in a developing embryo. To move, cells push out part of themselves called protrusions, use the protrusions to hold on to nearby tissues, and then haul them back in, as if casting out and hauling in an anchor to move forward.

VIDEO: A group of cells moves toward its correct final position in the tail of a forming zebrafish embryo. Cell membranes are green and the cell nuclei red.

This finding surprised us because we had no reason to suspect that the RhoA machinery required to move groups of cells would be different from that used by single cells, said senior study author Holger Knaut, PhD, associate professor in the Department of Cell Biology at NYU Langone Health.

Past studies had shown that single cells move forward in part by activating RhoA at their back ends. Active RhoA turns on the motor protein non-muscle myosin II, which causes the back ends of the cells to constrict and let go of the surface they are moving along on.

The current study found that the cells in the primordium instead activate RhoA in pulses in the front of the cells, where it does two jobs. At the front tip of the cell, RhoA grows the cell skeleton, called the actin meshwork, outward, forming protrusions that grip the surface. At the base of protrusions, RhoA triggers non-muscle myosin II to pull on the actin meshwork and haul in the protrusions. The pulling by myosin II makes the actin flow toward the center and back of the cells, pushing the cell group forward the way a banana slug moves along the ground.

Our findings suggest that RhoA-induced actin flow on the basal sides of cells constitutes the motor that pulls the primordium forward, a scenario that likely underlies the movement of many cell groups, added Dr. Knaut. The machinery suggests that the movement of single cells and groups of cells is similar, but that RhoA contributes to that machinery differently in each case. Within moving cell groups, RhoA generates actin flow directed toward the rear to propel the group forward.

Dr. Knaut notes that a better understanding of the mechanisms by which cell groups move has the potential to be useful in stopping the spread of cancer, perhaps by guiding the design of treatments that block the action of proteins noted in the study.

Along with Dr. Knaut, NYU Langone study authors were the co-corresponding author Weiyi Qian, PhD, Naoya Yamaguchi, and Patrycja Lis in the Department of Cell Biology, and Michael Cammer from the Microscopy Laboratory. The study was funded by Perlmutter Cancer Center Support Grant P30CA016087, National Institutes of Health grant R01NS119449, NYSTEM training grants C322560GG and C322560GG, two American Heart Association fellowships, 903886 and 20PRE3518016, and the NYU Deans Undergraduate Research Fund.

Greg Williams Phone: 212-404-3500 Gregory.Williams@NYULangone.org

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Cells Move in Groups Differently Than They Do When Alone - NYU Langone Health

Cell Biology

Cells move in groups differently than they do when alone – EurekAlert

December 14, 2023 medical

video:

Pictured here is a group of cells moving toward its correct final position in the tail of a forming zebrafish embryo. Cell membranes are green and the cell nuclei red.

Credit: Credit Holger Knaut, NYU Langone Health

A protein that helps generate the force needed for single cells to move works differently in cells moving in groups, a new study shows.

Cells push and pull on each other and surrounding tissue to move as they form organs in an embryo, heal wounds, track down invading bacteria, and become cancerous and spread. Led by researchers at NYU Grossman School of Medicine, the new study examined how forces are generated by a group of 140 cells called the primordium that adhere to each other as they move in zebrafish embryos. Zebrafish are a major model in the study of development because they are transparent and share cellular mechanisms with humans.

Published online December 13 in Current Biology, the new work reveals how the cells in the primordium use a protein called RhoA to trigger forces that move the group into place in the developing embryo. To move, cells push out part of themselves called protrusions, use the protrusions to hold on to nearby tissues, and then haul them back in to pull forward, like casting out and hauling in an anchor.

The current study found that the cells in the primordium instead activate RhoA in pulses in the front of the cells where it does two jobs. At the front tip of the cell, RhoA grows the cell skeleton, called the actin meshwork, outward, forming protrusions that grip the surface. At the base of protrusions, RhoA triggers non-muscle myosin II to pull on the actin meshwork and haul in the protrusions. The pulling by myosin II makes the actin flow toward the center and back of the cells, pushing the cell group forward the way a banana slug moves along the ground, but at a different size scale.

Along with Dr. Knaut, study authors were Weiyi Qian (co-corresponding author), Naoya Yamaguchi, and Patrycja Lis in the Department of Cell Biology, and Michael Cammer from the Microscopy Laboratory, at NYU Langone Health. The study was funded by Perlmutter Cancer Center Support Grant P30CA016087, National Institutions of Health grant R01NS119449, NYSTEM training grants C322560GG and C322560GG, two American Heart Association fellowships, 903886 and 20PRE3518016, and by the NYU Deans Undergraduate Research Fund.

Experimental study

Cells

Pulses of RhoA Signaling Stimulate Actin Polymerization and Flow in Protrusions to Drive Collective Cell Migration

13-Dec-2023

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Cells move in groups differently than they do when alone - EurekAlert

Cell Biology

Seattle Hub for Synthetic Biology plans to transform cells into tiny recording devices – GeekWire

December 14, 2023 medical

Jay Shendure, a professor of genome sciences at UW Medicine, will be executive director of the Seattle Hub for Synthetic Biology. (UW Medicine Photo)

The Allen Institute, the Chan Zuckerberg Initiative and the University of Washington have launched a collaboration called the Seattle Hub for Synthetic Biology, with the goal of using genetically modified cells to capture a DNA-based record showing how they change over time.

If the project works out as hoped, it could lead to a deeper understanding of the mechanisms behind cellular processes including, for example, how tumors grow and point to new methods for fighting disease and promoting healthy cell growth.

Over the next five years, the Seattle Hub for Synthetic Biology will receive $35 million from the Allen Institute, and another $35 million from the Chan Zuckerberg Initiative, founded by Meta CEO Mark Zuckerberg and his wife, Priscilla Chan.

Jay Shendure, a professor of genome sciences at UW Medicine, will serve as the hubs executive director. Other members of the leadership team include Marion Pepper and Cole Trapnell, researchers at UW Medicine; and Jesse Gray, a veteran of Ascidian Therapeutics and Harvard Medical School. The collaboration will build on technology pioneered at the Allen Discovery Center for Cell Lineage Tracing and the Brotman Baty Institute for Precision Medicine.

Shendure compared the genetically modified cells to flight recorders on airplanes. He said such cells could, for example, be combined with CAR-T cells to track the progress of cancer therapy.

You could imagine layering them into CAR-T cells to provide a record of what happened, in the context of trying to deliver a certain therapeutic, he told GeekWire. And then you could imagine components of these cells, or more sophisticated versions, actually being used as part of the therapy where, when and how a therapeutic turns on or off is modulated at some level by a much more sophisticated set of machinery.

That sort of application is far down the road. In the nearer term, SeaHubs researchers aim to develop a new channel for chronicling the changes that cells go through. This channel would take an approach thats different from existing methods that depend on microscope imaging or sequencing a cells entire genome.

Shendure and his colleagues at UW have already created two techniques that could help turn elements of the genetic machinery inside cells into tiny time-lapse recording devices.

One of the techniques, known as DNA Typewriter, was the subject of a research paper in the journal Nature last year. The system makes use of gene-editing tools to lay down short snippets of DNA in chronological order, moving along a molecular string like the clicks of the carriage on an old-fashioned typewriter.

If you insert a five-base-pair sequence, thats four to the fifth, or 1,024. So there are 1,024 possible symbols that we could insert, Shendure said. When you punch a key, so to speak, you write a symbol one of those 1,024 possible insertions. Thats like the recording of information. And the same edit moves the type head one unit down the tape. Youre not just firing letters at a piece of paper, youre actually typing them in some coherent order.

The second technique is Engram. Without Engram, DNA Typewriter is like a monkey at a typewriter, just hitting keys, Shendure said. But with Engram, at least for some of the keys, we can say youre more likely to type this key if this particular signaling pathway is active, or youre only going to type this key if youre this particular cell type. So, were starting to learn how to assign meanings to keys, and to build a vocabulary of triggers between biological signals and symbols on our keyboard.

To read the recording, researchers could extract some of the recorder cells and check the sequence of DNA letters that were inserted over time.

Early practical applications of the cell-recording technologies are likely to focus on studying how cells multiply and develop into tissues under normal conditions, and how things go wrong due to disease.

Studying the growth of a cancerous tumor would be a great example, Shendure said. If you want to probe the history of one tumor obviously this would be in a model organism, but it could be a human cell transplanted in a mouse trying to accumulate that history over time is something that you would want to do, he said.

Researchers could track the development of different tumors on the cellular level, and study how different treatment strategies affect their growth. For that scenario, a strain of mice could be genetically engineered with cell-recording capability.

We make a mouse line that essentially has all this stuff stably, and the recording device can be turned on at any point, Shendure said. You could have it constituently on, so it switches on at the beginning, or you could use a small chemical to turn it on, like doxycycline.

Such methods could also be used to fine-tune tissue engineering. If were trying to make skin in a dish, or something like that, whats working? Whats not working? And how do you modulate it to improve the process? Shendure said.

Using such techniques for clinical treatment in humans is a long-term strategy. But how long-term? I dont think theyre as futuristic as they might seem, given everything thats going on, Shendure said.

Findings from the research effort will be shared widely within the scientific community. Its all going to be open science, fitting with the philosophy of the Allen Institute and CZI, Shendure said.

The Chan Zuckerberg Initiatives backing for the Seattle Hub for Synthetic Biology builds on the philanthropic organizations history of supporting big-picture biotech projects including a $3 billion effort aimed at curing, preventing and managing all diseases within a generation, and $15 million in grants that were awarded in 2018 to support a global research effort called the Human Cell Atlas.

By developing new technologies to measure and understand the history of our cells over time, including how they are impacted by the environment around them, genetic mutations and other factors, we can expand scientists understanding of what happens at the cellular level when we go from healthy to sick, and help pinpoint the earliest causes of disease, CZI co-founder and co-CEO Priscilla Chan said in a news release.

Rui Costa, president and chief executive officer of the Allen Institute, said he and his colleagues are incredibly excited to enter this new era of collaboration to tackle big moonshot projects in partnership with others.

UW President Ana Mari Cauce said the project demonstrates the enormous potential impact of values-driven partnerships, and it represents a new way of thinking about how we can solve problems more quickly and effectively through scientific collaboration.

Our shared values, paired with our complementary perspectives and strengths, are a recipe for success, and I cant wait to see what this team will accomplish together, Cauce said.

The effort should yield noticeable results within five years, Shendure said.

It could lead to basically a library of tools for engineering cell types, specific expression, et cetera. I think therell be these deliverables that are broadly useful for the field, he said.

Shendure hopes researchers at the Seattle Hub for Synthetic Biology will come up with specific bodies of information relating to cell lineages, including cancer cell lineages, that would be impossible to obtain using more conventional technologies. But he also has a bigger goal in mind: Gaining acceptance for a new modality of measuring things over time, using DNA as a recording medium.

Thats been kind of a niche interest of technology development groups, Shendure said. Were trying to really move that toward the mainstream.

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Seattle Hub for Synthetic Biology plans to transform cells into tiny recording devices - GeekWire

Cell Biology

Virginia Tech and Weizmann Institute of Science tackle cell … – Virginia Tech

October 16, 2023 medical

In any fight, knowing your enemy is critical to staging a defense. The fight to stop cancer or to accelerate wound healing is no exception. The research teams at Virginia Tech and the Weizmann Institute of Israel, along with partners worldwide, are pursuing a deeper understanding of how cells move and spread throughout a living body.

Professor Amrinder Nain at Virginia Tech builds nanoscale suspended bridges to study cell migration. Professor Nir Gov at the Weizmann Institute develops the theoretical and computational framework for how cells migrate on curved surfaces. Their collaborative study combining state-of-the-art experiments and theory to examine cell coiling on fibers has been published in Nature Communications.

This study follows previous research partnering Gov and Nain for exploration of the inner mechanics of cancer. In that work, Nain and his partners from Virginia Tech, Japan, and Israel studied how a cells biology affects the motion of brain cancer cells. That work produced several novel discoveries, but chemistry and biology alone did not provide a complete picture. Needing a more holistic view of cellular behavior to understand how to halt cancer in its tracks, the team shifted from studying the inside of the cell to its outside, observing how it interacted with its environment.

Nain and Virginia Tech colleague Bahareh Behkam had previously identified a cell behavior called coiling, in which a cell wraps itself around a fiber axis to migrate. They found that coiling was more pronounced in cancerous invasive cells than their non-tumorigenic counterparts. Knowing this, they set out to understand the underlying energetic principles governing that coiling behavior.

Again needing Govs expertise, the team launched a new collaborative study with the team from Israel, aimed at discovering how a cell moves using its protrusions, or arm-like structures that extend outward from the front of a cell's soft body.

Nain and his collaborators knew these arms not only allow the cell to move, but also to grasp its environment and pull itself forward. The trick was to observe them in 3D at sufficient resolution. Virginia Tech team member Christian Hernandez-Padilla devised fiber networks and imaging strategies to capture coiling events. Nain then contacted Hari Shroff and Harshad Vishwasrao at the National Institutes of Health (NIH) to ask about using their lattice-light sheet advanced microscope.

We challenged ourselves to determine if coiling could be clearly observed in 3D for detailed measurements, said Nain. All it took was a cold email to Hari at NIH, to which he was extremely receptive. We were jubilant as Christians imaging data trickled in, showing cells coil on fibers in 3D.

In addition to the NIH, the team also reached out to:

Understanding cell migration requires knowing how cells curve around fibrous ropes the suspended bridges on which they eventually move. Nains expertise includes building nanoscale cellular suspended roadways that are fibrous. Compared to the flat landscape of a Petri dish, these fibers are much closer to the landscape of living tissues. By partnering with other experts, the team set the stage for illustrating how cells move inside a body, which could lead to new strategies to stop cancer cells or accelerate wound healing.

To propel itself, a cells jelly-like body first produces the tentacle-like protrusions. These cellular arms can grab onto things by twisting around fibers in the tissues surrounding them. But this behavior has rarely been studied before.

Recent imaging studies inside the body have shown cancer cells moving along individual fibers and navigating through varying fibrous architectures by reaching out and grabbing the fibers, Nain said. We combined our experiments with Nirs computational models to understand the energetics of coiling. This had never been attempted before, and it challenged our groups.

The group studied coiling on suspended fibers of various diameters, including flat ribbons pioneered in the Behkam lab. Researchers found that as a cell settled onto a fiber, its tentacle wrapped a few times around the fiber, giving the cell a firm grip. Hernandez-Padilla performed imaging at the NIH and developed the framework to quantify 3D coiling events from the voluminous data recorded.

In Israel, postdoctoral fellow Rajkumar Sadhu created a theoretical model that describes how a cell may get its shape and move when outside forces act on its membrane. Govs team found that energy minimization was a major driver. Picture a membrane trying to remain as flat as possible, avoiding sharp corners that would require more energy to navigate.

Complicated shapes such as the coiling result from protein complexes, themselves curved, bending the membrane as it follows their shape. Curved proteins also connect with the cytoskeleton, the structural component giving the cell its shape. The cytoskeleton grows and pushes outward during cellular movement, driving the protrusions.

These forces, arising from energy conservation and cytoskeleton dynamics, are responsible for the coiling. The model correctly predicted that the coiling would cease when the fiber had sharp corners, as in the case of the flat ribbons.

While this balance of energy between movement and cell biology happens in very small ways, it holds enormous implications for the future. Understanding how cells behave in their environment opens the door to understanding cell migration during developmental, disease, and repair biology.

In addition to the scientific advances of this project, Gov commented on the value of this work to the collaborative enterprise.

This collaboration already produced several publications and demonstrates how science is being done today through collaborations between people from different countries, continents, and ethnic and national backgrounds, he said. Beyond the curiosity and love of science, what unites us are the liberal ideals of freedom, human rights, and mutual respect and solidarity between all people.

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Virginia Tech and Weizmann Institute of Science tackle cell ... - Virginia Tech