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  • richardmitnick 10:08 am on March 29, 2018 Permalink | Reply
    Tags: , Biology, Bloomberg View, , , ,   

    From Rosetta@home via Bloomberg View: “Protein Engineering May Be the Future of Science” 

    Rosetta@home

    Rosetta@home

    2

    Bloomberg View

    March 27, 2018
    Faye Flam

    Some scientists think designing new proteins could become as significant as tweaking DNA.

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    Let’s build a better sperm whale. Photograph: SSPL/Getty Images

    Scientists are increasingly betting their time and effort that the way to control the world is through proteins. Proteins are what makes life animated. They take information encoded in DNA and turn it into intricate three-dimensional structures, many of which act as tiny machines. Proteins work to ferry oxygen through the bloodstream, extract energy from food, fire neurons, and attack invaders. One can think of DNA as working in the service of the proteins, carrying the information on how, when and in what quantities to make them.

    Living things make thousands of different proteins, but soon there could be many more, as scientists are starting to learn to design new ones from scratch with specific purposes in mind. Some are looking to design new proteins for drugs and vaccines, while others are seeking cleaner catalysts for the chemical industry and new materials.

    David Baker, director for the Institute for Protein Design at the University of Washington, compares protein design to the advent of custom tool-making. At some point, proto-humans went beyond merely finding uses for pieces of wood, rock or bone, and started designing tools to suit specific needs — from screwdrivers to sports cars.

    See the full article here.

    Please help promote STEM in your local schools.

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    Stem Education Coalition

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    U Washington Dr. David Baker

    Rosetta screensaver

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  • richardmitnick 10:58 am on March 21, 2018 Permalink | Reply
    Tags: Biology, , Drug Search for Leishmaniasis Project Continues Quest for Better Treatments, ,   

    From WCG: “Drug Search for Leishmaniasis Project Continues Quest for Better Treatments” 

    New WCG Logo

    WCGLarge

    World Community Grid (WCG)

    20 Mar 2018
    Dr. Carlos Muskus López
    Coordinator, Molecular Biology and Computational Unit, PECET University of Antioquia

    Summary
    The Drug Search for Leishmaniasis researchers recently conducted lab testing on 10 compounds. The testing showed that none of the compounds were good potential treatments, and the researchers will turn their attention to additional compounds.

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    Sandflies, such as the P. papatasi shown above, are responsible for the spread of leishmaniasis.

    Short description of the team’s latest findings

    Leishmaniasis is one of the most neglected tropical diseases in the world, infecting more than two million people in 98 countries. The current treatments for all forms of leishmaniasis can cause severe side effects, including death. Furthermore, drug resistant parasites are causing major problems in many countries. For these reasons, there is an urgent need for new, safe, and inexpensive drug compounds.

    The Drug Search for Leishmaniasis team has continued their lab testing since their last update. The most recent round of testing involved 10 compounds that had been identified as having potential to be safer, more effective treatments.

    The compounds were tested first for toxicity, then for effectiveness against two common parasites that can cause leishmaniasis. Based on the testing, none of the compounds tested would be effective treatments for the disease.

    The researchers will make these results public, as they have done with their data to-date. This will alert other scientists to the strong possibility that these particular compounds are not effective against leishmaniasis, and help them make decisions about testing other compounds. Once the team has obtained additional funding, they will test additional compounds that may be useful in treating leishmaniasis.

    Anyone interested in a full scientific description of this latest round of testing can read below. Thanks to everyone who supported this project.

    In vitro evaluation of the anti-leishmanial activity of predicted molecules by docking

    In order to determine if in silico predicted molecules with potential leishmanicidal activity could have the possibility of passing to in vivo assays, the molecules must first pass cytotoxicity testing against human cells in vitro. Then, those molecules that show low or no cytotoxicity are evaluated for parasite growth inhibition in human macrophages and the effective concentration 50 (EC50). The EC50 is the concentration of a molecule that kills 50% of the parasites in vitro.

    Evaluation of Anti-Leishmanial Activity

    Prior to the determination of the effective concentration 50 (EC50), all the compounds were pre-selected, by evaluating the effect on the percentage of infection in intracellular amastigotes in the U-937 cell line compared with amastigotes controls, in the absence of the compound.

    The activity of the compounds was evaluated on intracellular parasites (amastigote stage) obtained after in vitro infection of macrophages. The U-937 cells were infected with fluorescent promastigotes in stationary growth phase in a 30:1 parasite:cell ratio for the Leishmania panamensis UA140 strain and 20:1 for Leishmania braziliensis UA301 strain. The infected cells were exposed different concentration of the compounds for 72 hours (see the concentrations used for each compound, in a note below the Table 2). As infection control, infected cells were used in the absence of the compounds, and amphotericin B was used as a positive control. After 72 hours of incubation, the cells were carefully removed from the bottom of the dish and analyzed in a flow cytometer, reading at 488 nm excitation and 525 nm emission with an Argon4 laser.

    The anti-Leishmania activity was determined based on the parasite load, which is the number of parasites in the infected cells exposed to the concentration selected for each compound or amphotericin B. The decrease in parasite load, called inhibition of infection was calculated using the fluorescence mean intensity values €‹(MFI) and using the following formula: % Infection = [MFI cells infected and exposed to the compound or amphotericin B / MFI infected of unexposed cells] × 100). The MFI values ‹obtained for the infected cells in the absence of drug or compound corresponds to 100% of the infection. In turn, the percentage of inhibition of the infection corresponds to 100% of the infection -% infection in the presence of the compound.

    See the full article here.

    Ways to access the blog:
    https://sciencesprings.wordpress.com
    http://facebook.com/sciencesprings

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”
    WCG projects run on BOINC software from UC Berkeley.
    BOINCLarge

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BET!!

    My BOINC
    MyBOINC
    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Smash Childhood Cancer4

    FightAIDS@home Phase II

    FAAH Phase II
    OpenZika

    Rutgers Open Zika

    Help Stop TB
    WCG Help Stop TB
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    faah-1-new-screen-saver

    faah-1-new

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
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  • richardmitnick 10:40 am on March 9, 2018 Permalink | Reply
    Tags: , , Biology, , , WCG Microbiome Immunity Project   

    From WCG “Microbiome Immunity Project Already Extending the Known Universe of Protein Structures” 

    New WCG Logo

    WCGLarge

    World Community Grid (WCG)

    WCG Microbiome Immunity Project

    7 Mar 2018
    By: Tomasz Kosciolek, PhD
    UC San Diego Center for Microbiome Innovation

    Summary
    The Microbiome Immunity Project is off to a great start on predicting the structures of hundreds of thousands of bacterial proteins within the human gut. Read about their progress and their plans in their first project update.

    Background

    The Microbiome Immunity Project was created to better understand the role of the microbiome in intestinal immune response and diseases such as Type 1 Diabetes (T1D) and Inflammatory Bowel Disease (IBD). In this project, we predict structures of bacterial proteins and use this information to annotate their functions and to understand host-microbiome interactions which are responsible for the pathology of IBD and T1D. This is a massive undertaking, as the human gut microbiome has more than 2 million unique proteins, with hundreds of thousands of proteins potentially interacting with human cells. A project of this scale is only possible thanks to the power of World Community Grid.

    Our Progress So Far

    With your help, we have already predicted the structures of over 50,000 prioritized proteins! In the grand scheme of the 2 million unique bacterial proteins in our gut, this may not seem like a lot, but keep in mind that the experimental work to date covers only approximately 125,000 proteins. In only 6 months we have made tremendous progress by extending our universe of known protein structures by almost 28 percent!

    You may have already realized that at this pace, predicting all bacterial protein structures would take years to complete. Fortunately, we don’t have to predict every single structure, because proteins can be grouped into families. These families consist of proteins with similar structures and functions, enabling a comprehensive understanding of the family’s function with only one representative member per family. Once we identify protein families of interest, we will investigate them in more detail.

    In the meantime, we have adjusted our strategy on how to prioritize the predictions. Instead of looking only at bacterial genomes (genes of an individual bacterial species), we are investigating bacterial pangenomes (genes of all bacterial strains belonging to the same species). We then prioritize those pangenomes according to their prevalence between individuals in cohort studies investigating the role of microbiome in IBD and T1D. This approach enables us to have the most impact early in the project. We not only have thorough information on microbes involved in T1D and IBD specifically, but we have also expanded our knowledge of the microbiome in general.

    We are now extracting information from your predictions, and during the course of the project we plan to make the data available to the public for other exciting research. We are also working on methods to improve predictions of protein functions, enabling us to find the important protein families involved in T1D and IBD among thousands of predictions we have made so far.

    All this progress has been made possible thanks to your generous contributions! There is still a lot to discover about the microbiome, but with each computation that you support we are getting a step closer to having a more detailed picture of this important ecosystem inside each of our bodies and understanding IBD and T1D. So, thank you and let’s continue working together on unraveling the mysteries of microbiome!

    See the full article here.

    Ways to access the blog:
    https://sciencesprings.wordpress.com
    http://facebook.com/sciencesprings

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”
    WCG projects run on BOINC software from UC Berkeley.
    BOINCLarge

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BET!!

    My BOINC
    MyBOINC
    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Smash Childhood Cancer4

    FightAIDS@home Phase II

    FAAH Phase II
    OpenZika

    Rutgers Open Zika

    Help Stop TB
    WCG Help Stop TB
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    faah-1-new-screen-saver

    faah-1-new

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 10:05 pm on March 8, 2018 Permalink | Reply
    Tags: , , , , , Biology, , , , , , International Women's Day, , , , , ,   

    From PI: Women in STEM-“Celebrating International Women’s Day” 

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    Is it not a shame that we need to have a special day to celebrate women when they are so already fantastic and exceptionally brilliant in the physical sciences?

    Check out this blog post-
    https://sciencesprings.wordpress.com/2018/03/08/from-the-conversation-women-in-stem-perish-not-publish-new-study-quantifies-the-lack-of-female-authors-in-scientific-journals/

    “”I have done a couple of STEM events, but there have never been this many girls. There are so many here. It is really empowering. Go girls in STEM!” Eama, Grade 12

    Today’s Inspiring Future Women in Science conference was a success. Mona Nemar, Canada’s Chief Science Advisor, gave opening remarks encouraging the students in attendance to take advantage of the opportunity to learn from the speakers to come.

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    “The days of women being held back or being excluded from science are over. Now, more than ever women are entering, remaining in, and revolutionizing the science fields. Today is a shining example of that.”
    -Mona Nemar, Chief Science Advisor, Government of Canada

    Mona, read my above post on women getting not published.

    The speakers and panelists, who included a chemist, engineer, astronomer, ecologist, and surgeon, talked about the challenges and triumphs that a career in STEM brings. Students were then treated to a speed mentoring session where they were able to ask questions and interact with women from a broad number of STEM careers. Read more about how this conference is inspiring young women here.

    3
    “This conference showed me there are so many things you can do going into [a career in STEM], so now I feel more inspired, and I feel more confident and not scared to go into science.” Lealan, Age 16

    Programs like Perimeter’s “Inspiring Future Women in Science” conference are helping young women see their own potential and reach out for careers in STEM. And more talented female scientists today, means a brighter future tomorrow.

    Thank you for being part of the equation.
    4

     
  • richardmitnick 7:24 am on March 4, 2018 Permalink | Reply
    Tags: , Barbara Engelhardt, Biology, , , GTEx-Genotype-Tissue Expression Consortium, , ,   

    From Quanta Magazine: “A Statistical Search for Genomic Truths” 

    Quanta Magazine
    Quanta Magazine

    February 27, 2018
    Jordana Cepelewicz

    1
    Barbara Engelhardt, a Princeton University computer scientist, wants to strengthen the foundation of biological knowledge in machine-learning approaches to genomic analysis. Sarah Blesener for Quanta Magazine.

    We don’t have much ground truth in biology.” According to Barbara Engelhardt, a computer scientist at Princeton University, that’s just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Techniques in artificial intelligence and machine learning are dramatically altering the landscape of biological research, but Engelhardt doesn’t think those “black box” approaches are enough to provide the insights necessary for understanding, diagnosing and treating disease. Instead, she’s been developing new statistical tools that search for expected biological patterns to map out the genome’s real but elusive “ground truth.”

    Engelhardt likens the effort to detective work, as it involves combing through constellations of genetic variation, and even discarded data, for hidden gems. In research published last October [Nature], for example, she used one of her models to determine how mutations relate to the regulation of genes on other chromosomes (referred to as distal genes) in 44 human tissues. Among other findings, the results pointed to a potential genetic target for thyroid cancer therapies. Her work has similarly linked mutations and gene expression to specific features found in pathology images.

    The applications of Engelhardt’s research extend beyond genomic studies. She built a different kind of machine-learning model, for instance, that makes recommendations to doctors about when to remove their patients from a ventilator and allow them to breathe on their own.

    She hopes her statistical approaches will help clinicians catch certain conditions early, unpack their underlying mechanisms, and treat their causes rather than their symptoms. “We’re talking about solving diseases,” she said.

    To this end, she works as a principal investigator with the Genotype-Tissue Expression (GTEx) Consortium, an international research collaboration studying how gene regulation, expression and variation contribute to both healthy phenotypes and disease.

    2

    Right now, she’s particularly interested in working on neuropsychiatric and neurodegenerative diseases, which are difficult to diagnose and treat.

    Quanta Magazine recently spoke with Engelhardt about the shortcomings of black-box machine learning when applied to biological data, the methods she’s developed to address those shortcomings, and the need to sift through “noise” in the data to uncover interesting information. The interview has been condensed and edited for clarity.

    What motivated you to focus your machine-learning work on questions in biology?

    I’ve always been excited about statistics and machine learning. In graduate school, my adviser, Michael Jordan [at the University of California, Berkeley], said something to the effect of: “You can’t just develop these methods in a vacuum. You need to think about some motivating applications.” I very quickly turned to biology, and ever since, most of the questions that drive my research are not statistical, but rather biological: understanding the genetics and underlying mechanisms of disease, hopefully leading to better diagnostics and therapeutics. But when I think about the field I am in — what papers I read, conferences I attend, classes I teach and students I mentor — my academic focus is on machine learning and applied statistics.

    We’ve been finding many associations between genomic markers and disease risk, but except in a few cases, those associations are not predictive and have not allowed us to understand how to diagnose, target and treat diseases. A genetic marker associated with disease risk is often not the true causal marker of the disease — one disease can have many possible genetic causes, and a complex disease might be caused by many, many genetic markers possibly interacting with the environment. These are all challenges that someone with a background in statistical genetics and machine learning, working together with wet-lab scientists and medical doctors, can begin to address and solve. Which would mean we could actually treat genetic diseases — their causes, not just their symptoms.

    You’ve spoken before about how traditional statistical approaches won’t suffice for applications in genomics and health care. Why not?

    First, because of a lack of interpretability. In machine learning, we often use “black-box” methods — [classification algorithms called] random forests, or deeper learning approaches. But those don’t really allow us to “open” the box, to understand which genes are differentially regulated in particular cell types or which mutations lead to a higher risk of a disease. I’m interested in understanding what’s going on biologically. I can’t just have something that gives an answer without explaining why.

    The goal of these methods is often prediction, but given a person’s genotype, it is not particularly useful to estimate the probability that they’ll get Type 2 diabetes. I want to know how they’re going to get Type 2 diabetes: which mutation causes the dysregulation of which gene to lead to the development of the condition. Prediction is not sufficient for the questions I’m asking.

    A second reason has to do with sample size. Most of the driving applications of statistics assume that you’re working with a large and growing number of data samples — say, the number of Netflix users or emails coming into your inbox — with a limited number of features or observations that have interesting structure. But when it comes to biomedical data, we don’t have that at all. Instead, we have a limited number of patients in the hospital, a limited number of genotypes we can sequence — but a gigantic set of features or observations for any one person, including all the mutations in their genome. Consequently, many theoretical and applied approaches from statistics can’t be used for genomic data.

    What makes the genomic data so challenging to analyze?

    The most important signals in biomedical data are often incredibly small and completely swamped by technical noise. It’s not just about how you model the real, biological signal — the questions you’re trying to ask about the data — but also how you model that in the presence of this incredibly heavy-handed noise that’s driven by things you don’t care about, like which population the individuals came from or which technician ran the samples in the lab. You have to get rid of that noise carefully. And we often have a lot of questions that we would like to answer using the data, and we need to run an incredibly large number of statistical tests — literally trillions — to figure out the answers. For example, to identify an association between a mutation in a genome and some trait of interest, where that trait might be the expression levels of a specific gene in a tissue. So how can we develop rigorous, robust testing mechanisms where the signals are really, really small and sometimes very hard to distinguish from noise? How do we correct for all this structure and noise that we know is going to exist?

    So what approach do we need to take instead?

    My group relies heavily on what we call sparse latent factor models, which can sound quite mathematically complicated. The fundamental idea is that these models partition all the variation we observed in the samples, with respect to only a very small number of features. One of these partitions might include 10 genes, for example, or 20 mutations. And then as a scientist, I can look at those 10 genes and figure out what they have in common, determine what this given partition represents in terms of a biological signal that affects sample variance.

    So I think of it as a two-step process: First, build a model that separates all the sources of variation as carefully as possible. Then go in as a scientist to understand what all those partitions represent in terms of a biological signal. After this, we can validate those conclusions in other data sets and think about what else we know about these samples (for instance, whether everyone of the same age is included in one of these partitions).

    When you say “go in as a scientist,” what do you mean?

    I’m trying to find particular biological patterns, so I build these models with a lot of structure and include a lot about what kinds of signals I’m expecting. I establish a scaffold, a set of parameters that will tell me what the data say, and what patterns may or may not be there. The model itself has only a certain amount of expressivity, so I’ll only be able to find certain types of patterns. From what I’ve seen, existing general models don’t do a great job of finding signals we can interpret biologically: They often just determine the biggest influencers of variance in the data, as opposed to the most biologically impactful sources of variance. The scaffold I build instead represents a very structured, very complex family of possible patterns to describe the data. The data then fill in that scaffold to tell me which parts of that structure are represented and which are not.

    So instead of using general models, my group and I carefully look at the data, try to understand what’s going on from the biological perspective, and tailor our models based on what types of patterns we see.

    How does the latent factor model work in practice?

    We applied one of these latent factor models to pathology images [pictures of tissue slices under a microscope], which are often used to diagnose cancer. For every image, we also had data about the set of genes expressed in those tissues. We wanted to see how the images and the corresponding gene expression levels were coordinated.

    We developed a set of features describing each of the images, using a deep-learning method to identify not just pixel-level values but also patterns in the image. We pulled out over a thousand features from each image, give or take, and then applied a latent factor model and found some pretty exciting things.

    For example, we found sets of genes and features in one of these partitions that described the presence of immune cells in the brain. You don’t necessarily see these cells on the pathology images, but when we looked at our model, we saw a component there that represented only genes and features associated with immune cells, not brain cells. As far as I know, no one’s seen this kind of signal before. But it becomes incredibly clear when we look at these latent factor components.


    Video: Barbara Engelhardt, a computer scientist at Princeton University, explains why traditional machine-learning techniques have often fallen short for genomic analysis, and how researchers are overcoming that challenge. Sarah Blesener for Quanta Magazine

    You’ve worked with dozens of human tissue types to unpack how specific genetic variations help shape complex traits. What insights have your methods provided?

    We had 44 tissues, donated from 449 human cadavers, and their genotypes (sequences of their whole genomes). We wanted to understand more about the differences in how those genotypes expressed their genes in all those tissues, so we did more than 3 trillion tests, one by one, comparing every mutation in the genome with every gene expressed in each tissue. (Running that many tests on the computing clusters we’re using now takes about two weeks; when we move this iteration of GTEx to the cloud as planned, we expect it to take around two hours.) We were trying to figure out whether the [mutant] genotype was driving distal gene expression. In other words, we were looking for mutations that weren’t located on the same chromosome as the genes they were regulating. We didn’t find very much: a little over 600 of these distal associations. Their signals were very low.

    But one of the signals was strong: an exciting thyroid association, in which a mutation appeared to distally regulate two different genes. We asked ourselves: How is this mutation affecting expression levels in a completely different part of the genome? In collaboration with Alexis Battle’s lab at Johns Hopkins University, we looked near the mutation on the genome and found a gene called FOXE1, for a transcription factor that regulates the transcription of genes all over the genome. The FOXE1 gene is only expressed in thyroid tissues, which was interesting. But we saw no association between the mutant genotype and the expression levels of FOXE1. So we had to look at the components of the original signal we’d removed before — everything that had appeared to be a technical artifact — to see if we could detect the effects of the FOXE1 protein broadly on the genome.

    We found a huge impact of FOXE1 in the technical artifacts we’d removed. FOXE1, it seems, regulates a large number of genes only in the thyroid. Its variation is driven by the mutant genotype we found. And that genotype is also associated with thyroid cancer risk. We went back to the thyroid cancer samples — we had about 500 from the Cancer Genome Atlas — and replicated the distal association signal. These things tell a compelling story, but we wouldn’t have learned it unless we had tried to understand the signal that we’d removed.

    What are the implications of such an association?

    Now we have a particular mechanism for the development of thyroid cancer and the dysregulation of thyroid cells. If FOXE1 is a druggable target — if we can go back and think about designing drugs to enhance or suppress the expression of FOXE1 — then we can hope to prevent people at high thyroid cancer risk from getting it, or to treat people with thyroid cancer more effectively.

    The signal from broad-effect transcription factors like FOXE1 actually looks a lot like the effects we typically remove as part of the noise: population structure, or the batches the samples were run in, or the effects of age or sex. A lot of those technical influences are going to affect approximately similar numbers of genes — around 10 percent — in a similar way. That’s why we usually remove signals that have that pattern. In this case, though, we had to understand the domain we were working in. As scientists, we looked through all the signals we’d gotten rid of, and this allowed us to find the effects of FOXE1 showing up so strongly in there. It involved manual labor and insights from a biological background, but we’re thinking about how to develop methods to do it in a more automated way.

    So with traditional modeling techniques, we’re missing a lot of real biological effects because they look too similar to noise?

    Yes. There are a ton of cases in which the interesting pattern and the noise look similar. Take these distal effects: Pretty much all of them, if they are broad effects, are going to look like the noise signal we systematically get rid of. It’s methodologically challenging. We have to think carefully about how to characterize when a signal is biologically relevant or just noise, and how to distinguish the two. My group is working fairly aggressively on figuring that out.

    Why are those relationships so difficult to map, and why look for them?

    There are so many tests we have to do; the threshold for the statistical significance of a discovery has to be really, really high. That creates problems for finding these signals, which are often incredibly small; if our threshold is that high, we’re going to miss a lot of them. And biologically, it’s not clear that there are many of these really broad-effect distal signals. You can imagine that natural selection would eliminate the kinds of mutations that affect 10 percent of genes — that we wouldn’t want that kind of variability in the population for so many genes.

    But I think there’s no doubt that these distal associations play an enormous role in disease, and that they may be considered as druggable targets. Understanding their role broadly is incredibly important for human health.

    See the full article here .

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    Formerly known as Simons Science News, Quanta Magazine is an editorially independent online publication launched by the Simons Foundation to enhance public understanding of science. Why Quanta? Albert Einstein called photons “quanta of light.” Our goal is to “illuminate science.” At Quanta Magazine, scientific accuracy is every bit as important as telling a good story. All of our articles are meticulously researched, reported, edited, copy-edited and fact-checked.

     
  • richardmitnick 8:50 pm on February 20, 2018 Permalink | Reply
    Tags: , , Biology, PLANTS COLONIZED THE EARTH 100 MILLION YEARS EARLIER THAN PREVIOUSLY THOUGHT   

    From Astrobiology Magazine: “PLANTS COLONIZED THE EARTH 100 MILLION YEARS EARLIER THAN PREVIOUSLY THOUGHT” 

    Astrobiology Magazine

    Astrobiology Magazine

    Feb 20, 2018

    1

    For the first four billion years of Earth’s history, our planet’s continents would have been devoid of all life except microbes.

    All of this changed with the origin of land plants from their pond scum relatives, greening the continents and creating habitats that animals would later invade.

    The timing of this episode has previously relied on the oldest fossil plants which are about 420 million years old.

    New research, published in the journal Proceedings of the National Academy of Sciences USA, indicates that these events actually occurred a hundred million years earlier, changing perceptions of the evolution of the Earth’s biosphere.

    Plants are major contributors to the chemical weathering of continental rocks, a key process in the carbon cycle that regulates Earth’s atmosphere and climate over millions of years.

    The team used ‘molecular clock’ methodology, which combined evidence on the genetic differences between living species and fossil constraints on the age of their shared ancestors, to establish an evolutionary timescale that sees through the gaps in the fossil record.

    Dr Jennifer Morris, from the University of Bristol’s School of Earth Sciences and co-lead author on the study, explained: “The global spread of plants and their adaptations to life on land, led to an increase in continental weathering rates that ultimately resulted in a dramatic decrease the levels of the ‘greenhouse gas’ carbon dioxide in the atmosphere and global cooling.

    “Previous attempts to model these changes in the atmosphere have accepted the plant fossil record at face value – our research shows that these fossil ages underestimate the origins of land plants, and so these models need to be revised.”

    Co-lead author Mark Puttick described the team’s approach to produce the timescale. He said: “The fossil record is too sparse and incomplete to be a reliable guide to date the origin of land plants. Instead of relying on the fossil record alone, we used a ‘molecular clock’ approach to compare differences in the make-up of genes of living species – these relative genetic differences were then converted into ages by using the fossil ages as a loose framework.

    “Our results show the ancestor of land plants was alive in the middle Cambrian Period, which was similar to the age for the first known terrestrial animals.”

    One difficulty in the study is that the relationships between the earliest land plants are not known. Therefore the team, which also includes members from Cardiff University and the Natural History Museum, London, explored if different relationships changed the estimated origin time for land plants.

    Leaders of the overall study, Professor Philip Donoghue and Harald Schneider added: “We used different assumptions on the relationships between land plants and found this did not impact the age of the earliest land plants.

    “Any future attempts to model atmospheric changes in deep-time must incorporate the full range of uncertainties we have used here.”

    See the full article here .

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    NASA

     
  • richardmitnick 10:19 am on February 19, 2018 Permalink | Reply
    Tags: , Biology, C1 complex, CryoEM-cryo electron microscopy, CryoET-Cryo electron tomography, U Utrecht   

    From U Utrecht: “Unexpected immune activation illustrated in the cold” 

    Utrecht University

    15 February 2018

    Monica van der Garde
    Public Information Officer
    m.vandergarde@uu.nl
    +31 (0)6 13 66 14 38

    Press Office Leiden University Medical Center
    pers@lumc.nl
    +31 6 11 37 11 46
    +31 71 526 8005

    q
    Combining CryoEM and CryoET lets researchers see the C1 complex in 3D (coloured model) bound to antibodies in a native state (background).

    Researchers at Utrecht University and Leiden University Medical Center, the Netherlands, have for the first time made a picture of an important on-switch of our immune system. Their novel technical approach already led to the discovery of not one, but two ways in which the immune system can be activated. This kind of new insights are important for designing better therapies against infections or cancer, according to team leaders Piet Gros and Thom Sharp. Their findings are published on February 16, 2018 in the journal Science.

    When invading microbes, viruses and tumours are detected in our bodies, our antibodies engage in an immediate defence strategy. They quickly raise warning signs on these aberrant surfaces that alert our body’s immune system of a security breach. This is the entry cue of several molecules, together called the C1 complex, that stick to the surface of the rogue cell and eliminate it from our body. Until recently, it was unknown how exactly invaders were recognized, and how this C1 complex was activated.

    Challenging

    Studying the C1 complex has been challenging since its components often clump together when taken out of their natural environment into a lab setting. Together with the international biotech company Genmab A/S, researchers from Utrecht University and Leiden University Medical Center have now developed a unique technical approach to studying it in a more natural environment – and discovered more than expected.

    Life-like detailed picture

    In order to capture the binding and interaction of the complex, Piet Gros, Utrecht University and Thom Sharp, Leiden University Medical Center, combined two imaging techniques, cryo electron microscopy (CryoEM) and cryo electron tomography (CryoET). “These technologies are exploding in the field,” describes Thom Sharp, “and each method gives us different but complementary information on the same complex.” When combined, these methods provide a more life-like detailed picture of the system.

    Reconstruction into a 3D representation

    For CryoEM, think of taking thousands of copies of the same convoluted complex and scattering them onto the sticky side of a piece of tape. The camera is in a fixed position and takes pictures of these particles, which may have landed right-side-up, on its side, on a point. CryoET, on the other hand, can image the complex in a more natural environment, as it is bound to the cell surface. It takes images from different angles of the complex, similar to a CT scan, where the particle rotates within the instrument. For both techniques, images are then reconstructed into a 3D representation of the complex.

    Very different mechanisms identified

    The researchers were surprised to find not one, but two ways in which the immune system can be activated: by physical distortion and by cross-activation. In some cases, the configuration of danger signals on a cell’s surface is sparse, and when antibodies bind, the entire complex must physically adjust or distort itself to properly fit. This adjustment of a single complex can set off an immune response. In other situations, where the danger signals are dense, multiple C1 complexes can help activate each other, like a neighbourhood watch system.

    First report

    This is the first report of two independent ways by which our immune system can be activated. In addition, the combination of CryoEM and CryoET enabled the visualization of details of these interactions that may enable researchers to create more specific therapeutics that can activate, slow down or stop the cascade of signals within our immune system.

    See the full article here .

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    Utrecht University (UU; Dutch: Universiteit Utrecht, formerly Rijksuniversiteit Utrecht) is a university in Utrecht, the Netherlands. It is one of the oldest universities in the Netherlands. Established March 26, 1636, it had an enrollment of 29,425 students in 2016, and employed 5,568 faculty and staff.[4] In 2011, 485 PhD degrees were awarded and 7,773 scientific articles were published. The 2013 budget of the university was €765 million.[5]

    The university is rated as the best university in the Netherlands by the Shanghai Ranking of World Universities 2013, and ranked as the 13th best university in Europe and the 52nd best university of the world.

    The university’s motto is “Sol Iustitiae Illustra Nos,” which means “Sun of Justice, shine upon us.” This motto was gleaned from a literal Latin Bible translation of Malachi 4:2. (Rutgers University, having a historical connection with Utrecht University, uses a modified version of this motto.) Utrecht University is led by the University Board, consisting of prof. dr. Bert van der Zwaan (Rector Magnificus) and Hans Amman.

     
  • richardmitnick 9:11 am on February 19, 2018 Permalink | Reply
    Tags: , Biology, Electric Eels, Electrocytes, ,   

    From The Atlantic: “A New Kind of Soft Battery, Inspired by the Electric Eel” 

    Atlantic Magazine

    The Atlantic Magazine

    Dec 13, 2017
    Ed Yong

    1
    Thomas Schroeder / Anirvan Guha

    In 1799, the Italian scientist Alessandro Volta fashioned an arm-long stack of zinc and copper discs, separated by salt-soaked cardboard. This “voltaic pile” was the world’s first synthetic battery, but Volta based its design on something far older—the body of the electric eel.

    This infamous fish makes its own electricity using an electric organ that makes up 80 percent of its two-meter length. The organ contains thousands of specialized muscle cells called Electric Eel. Each only produces a small voltage, but together, they can generate up to 600 volts—enough to stun a human, or even a horse. They also provided Volta with ideas for his battery, turning him into a 19th-century celebrity.

    Two centuries on, and batteries are everyday objects. But even now, the electric eel isn’t done inspiring scientists. A team of researchers led by Michael Mayer at the University of Fribourg have now created a new kind of power source [Nature] that ingeniously mimics the eel’s electric organ. It consists of blobs of multicolored gels, arranged in long rows much like the eel’s electrocytes. To turn this battery on, all you need to do is to press the gels together.

    Unlike conventional batteries, the team’s design is soft and flexible, and might be useful for powering the next generation of soft-bodied robots. And since it can be made from materials that are compatible with our bodies, it could potentially drive the next generation of pacemakers, prosthetics, and medical implants. Imagine contact lenses that generate electric power, or pacemakers that run on the fluids and salts within our own bodies—all inspired by a shocking fish.

    To create their unorthodox battery, the team members Tom Schroeder and Anirvan Guha began by reading up on how the eel’s electrocytes work. These cells are stacked in long rows with fluid-filled spaces between them. Picture a very tall tower of syrup-smothered pancakes, turned on its side, and you’ll get the idea.

    When the eel’s at rest, each electrocyte pumps positively charged ions out of both its front-facing and back-facing sides. This creates two opposing voltages that cancel each other out. But at the eel’s command, the back side of each electrocyte flips, and starts pumping positive ions in the opposite direction, creating a small voltage across the entire cell. And crucially, every electrocyte performs this flip at the same time, so their tiny voltages add up to something far more powerful. It’s as if the eel has thousands of small batteries in its tail; half are pointing in the wrong direction but it can flip them at a whim, so that all of them align. “It’s insanely specialized,” says Schroeder.

    2
    How an electric eel’s electrocytes work (Schroeder et al. / Nature).

    He and his colleagues first thought about re-creating the entire electric organ in a lab, but soon realized that it’s far too complicated. Next, they considered setting up a massive series of membranes to mimic the stacks of electrocytes—but these are delicate materials that are hard to engineer in the thousands. If one broke, the whole series would shut down. “You’d run into the string-of-Christmas-lights problem,” says Schroeder.

    In the end, he and Guha opted for a much simpler setup, involving lumps of gel that are arranged on two separate sheets. Look at the image below, and focus on the bottom sheet. The red gels contain saltwater, while blue ones contain freshwater. Ions would flow from the former to the latter, but they can’t because the gels are separated. That changes when the green and yellow gels on the other sheet bridge the gaps between the blue and red ones, providing channels through which ions can travel.

    Here’s the clever bit: The green gel lumps only allow positive ions to flow through them, while the yellow ones only let negative ions pass. This means (as the inset in the image shows) that positive ions flow into the blue gels from only one side, while negative ions flow in from the other. This creates a voltage across the blue gel, exactly as if it was an electrocyte. And just as in the electrocytes, each gel only produces a tiny voltage, but thousands of them, arranged in a row, can produce up to 110 volts

    3
    Schroeder et al. / Nature.

    The eel’s electrocytes fire when they receive a signal from the animal’s neurons. But in Schroeder’s gels, the trigger is far simpler—all he needs to do is to press the gels together.

    It would be cumbersome to have incredibly large sheets of these gels. But Max Shtein, an engineer at the University of Michigan, suggested a clever solution—origami. Using a special folding pattern that’s also used to pack solar panels into satellites, he devised a way of folding a flat sheet of gels so the right colors come into contact in the right order. That allowed the team to generate the same amount of power in a much smaller space—in something like a contact lens, which might one day be realistically worn.

    For now, such batteries would have to be actively recharged. Once activated, they produce power for up to a few hours, until the levels of ions equalize across the various gels, and the battery goes flat. You then need to apply a current to reset the gels back to alternating rows of high-salt and low-salt. But Schroeder notes that our bodies constantly replenish reservoirs of fluid with varying levels of ions. He imagines that it might one day be possible to harness these reservoirs to create batteries.

    Essentially, that would turn humans into something closer to an electric eel. It’s unlikely that we’d ever be able to stun people, but we could conceivably use the ion gradients in our own bodies to power small implants. Of course, Schroeder says, that’s still more a flight of fancy than a goal he has an actual road map for. “Plenty of things don’t work for all sorts of reasons, so I don’t want to get too far ahead of myself,” he says.

    It’s not unreasonable to speculate, though, says Ken Catania from Vanderbilt University, who has spent years studying the biology of the eels. “Volta’s battery was not exactly something you could fit in a cellphone, but over time we have all come to depend on it,” he says. “Maybe history will repeat itself.”

    “I’m amazed at how much electric eels have contributed to science,” he adds. “It’s a good lesson in the value of basic science.” Schroeder, meanwhile, has only ever seen electric eels in zoos, and he’d like to encounter one in person. “I’ve never been shocked by one, but I feel like I should at some point,” he says.

    See the full article here .

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  • richardmitnick 5:47 pm on February 15, 2018 Permalink | Reply
    Tags: , Biology, , Cells Communicate in a Dynamic Code   

    From Caltech: “Cells Communicate in a Dynamic Code” 

    Caltech Logo

    Caltech

    02/15/2018

    Lori Dajose
    (626) 395-1217
    ldajose@caltech.edu

    1
    Artist’s concept of a cell expressing the Delta1 ligand (left) and a cell expressing the Delta4 ligand (right). While these two ligands activate cellular receptors in the same way, they do so in different patterns over time. In this way, a receiving cell can decode instructions.
    Credit: Caltech

    2
    Illustration of a message-receiving cell (bottom) expressing the Notch receptor, depicted as a satellite dish, to receive messages from cells expressing two different ligand “messages”, named Delta1 (left, blue) or Delta4 (right, red). The identity of the signaling molecule is encoded in pulsatile (left) or sustained (right) dynamics. These dynamics are in turn “decoded” by a module involving the genes HES1 and HEY1 (white box with dials) to control the cell’s decision to differentiate. Credit: Courtesy of the Elowitz laboratory

    A critically important intercellular communication system is found to encode and transmit more messages than previously thought.

    Multicellular organisms like ourselves depend on a constant flow of information between cells, coordinating their activities in order to proliferate and differentiate. Deciphering the language of intercellular communication has long been a central challenge in biology. Now, Caltech scientists have discovered that cells have evolved a way to transmit more messages through a single pathway, or communication channel, than previously thought, by encoding the messages rhythmically over time.

    The work, conducted in the laboratory of Michael Elowitz, professor of biology and bioengineering, Howard Hughes Medical Institute Investigator, and executive officer for Biological Engineering, is described in a paper in the February 8 issue of Cell.

    In particular, the scientists studied a key communication system called “Notch,” which is used in nearly every tissue in animals. Malfunctions in the Notch pathway contribute to a variety of cancers and developmental diseases, making it a desirable target to study for drug development.

    Cells carry out their conversations using specialized communication molecules called ligands, which interact with corresponding molecular antennae called receptors. When a cell uses the Notch pathway to communicate instructions to its neighbors—telling them to divide, for example, or to differentiate into a different kind of cell—the cell sending the message will produce certain Notch ligands on its surface. These ligands then bind to Notch receptors embedded in the surface of nearby cells, triggering the receptors to release gene-modifying molecules called transcription factors into the interior of their cell. The transcription factors travel to the cell’s nucleus, where the cell’s DNA is stored, and activate specific genes. The Notch system thus allows cells to receive signals from their neighbors and alter their gene expression accordingly.

    Ligands prompt the activation of transcription factors by modifying the structure of the receptors into which they dock. All ligands modify their receptors in a similar way and activate the same transcription factors in a receiving cell, and for that reason, scientists generally assumed that the receiving cell should not be able to reliably determine which ligand had activated it, and hence which message it had received.

    “At first glance, the only explanation for how cells distinguish between two ligands, if at all, seems to be that they must somehow accurately detect differences in how strongly the two ligands activate the receptor. However, all evidence so far suggests that, unlike mobile phones or radios, cells have much more trouble precisely analyzing incoming signals,” says lead author and former Elowitz lab graduate student Nagarajan (Sandy) Nandagopal (PhD ’18). “They are usually excellent at distinguishing between the presence or absence of signal, but not very much more. In this sense, cellular messaging is closer to sending smoke signals than texting. So, the question is, as a cell, how do you differentiate between two ligands, both of which look like similar puffs of smoke in the distance?”

    Nandagopal and his collaborators wondered whether the answer lay in the temporal pattern of Notch activation by different ligands—in other words, how the “smoke” is emitted over time. To test this, the team developed a new video-based system through which they could record signaling in real time in each individual cell. By tagging the receptors and ligands with fluorescent protein markers, the team could watch how the molecules interacted as signaling was occurring.

    The team studied two chemically similar Notch ligands, dubbed Delta1 and Delta4. They discovered that despite the ligands’ similarity the two activated the same receptor with strikingly different temporal patterns. Delta1 ligands activated clusters of receptors simultaneously, sending a sudden burst of transcription factors down to the nucleus all at once, like a smoke signal consisting of a few giant puffs. On the other hand, Delta4 ligands activated individual receptors in a sustained manner, sending a constant trickle of single transcription factors to the nucleus, like a steady stream of smoke.

    These two patterns are the key to encoding different instructions to the cell, the researchers say. In fact, this mechanism enabled the two ligands to communicate dramatically different messages. By analyzing chick embryos, the authors discovered that Delta1 activated abdominal muscle production, whereas Delta4 strongly inhibited this process in the same cells.

    “Cells speak only a handful of different molecular languages but they have to work together to carry out an incredible diversity of tasks,” says Elowitz. “We’ve generally assumed these languages are extremely simple, and cells can basically only grunt at each other. By watching cells in the process of communicating, we can see that these languages are more sophisticated and have a larger vocabulary than we ever thought. And this is probably just the tip of an iceberg for intercellular communication.”

    The paper is titled Dynamic Ligand Discrimination in the Notch Signaling Pathway. In addition to Nandagopal and Elowitz, other Caltech co-authors are Leah Santat, who is also a Howard Hughes Medical Institute Investigator, and Marianne Bronner, the Albert Billings Ruddock Professor of Biology. Additional co-authors are Lauren LeBon of Calico Life Sciences and David Sprinzak of Tel Aviv University. Funding was provided by the Defense Advanced Research Projects Agency, the National Institutes of Health, the National Science Foundation, and the Howard Hughes Medical Institute.

    See the full article here .

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    The California Institute of Technology (commonly referred to as Caltech) is a private research university located in Pasadena, California, United States. Caltech has six academic divisions with strong emphases on science and engineering. Its 124-acre (50 ha) primary campus is located approximately 11 mi (18 km) northeast of downtown Los Angeles. “The mission of the California Institute of Technology is to expand human knowledge and benefit society through research integrated with education. We investigate the most challenging, fundamental problems in science and technology in a singularly collegial, interdisciplinary atmosphere, while educating outstanding students to become creative members of society.”

    Caltech campus

     
  • richardmitnick 1:47 pm on February 15, 2018 Permalink | Reply
    Tags: , Biology, , , Life and death of proteins   

    From EMBL: “Life and death of proteins” 

    EMBL European Molecular Biology Laboratory bloc

    European Molecular Biology Laboratory

    15 February 2018
    Berta Carreño

    EMBL scientists create a turnover catalogue of almost 10.000 proteins from primary cells

    1
    Architecture dependent turnover of the nuclear pore subunits. Top row shows the nuclear pore subunits seen from top, bottom row shows subunits of the nuclear pore cut in half. IMAGE: Jan Kosinski/EMBL.

    Proteins perform countless functions in the cell, including transporting molecules, speeding up metabolic reactions and forming structural parts of the cell such as the nuclear pore complex. Protein turnover is a measure of the difference between protein synthesis and protein degradation and it is an important indicator of a cell’s activity in health and disease.

    EMBL group leaders Mikhail Savitski and Martin Beck, in close collaboration with Cellzome scientists Marcus Bantscheff and Toby Mathieson, have improved the accuracy of the detection of small changes in protein turnover by developing a better algorithmic treatment of raw mass spectrometry data. As a result, the researchers have published a turnover catalogue of 9699 unique proteins in Nature Communications. The paper focuses on protein complexes and demonstrates that subunits of protein complexes have consistent turnover rates.

    What did you do?

    We wanted to study protein homeostasis, or the balanced process behind protein synthesis and degradation in primary cells extracted from blood or living tissue. Primary cells provide a better understanding of the in vivo situation than cultured cells but, unfortunately, they have a short lifespan when compared to the protein complexes we wanted to study. To overcome this problem, we developed a better algorithmic treatment of raw mass spectrometry data. The improved algorithm accurately determines very small changes in proteins, allowing us to measure the turnover of 9699 unique proteins, including very long-lived proteins, such as the Histone H1.2 protein which has a half-life of 2242 hours. For the first time, we have a view of protein turnover at a cellular scale in several primary cell types, which will be a valuable resource for the scientific community.

    We focused our analysis on protein complexes, particularly on the nuclear pore complex, which is very big and is composed of several sub-complexes. We discovered that there are protein turnover levels that are specific to a given sub-complex. Proteins which are peripheral to the complex, that joined later in evolution, turn out to have much faster turnover than the ones that form the core structure and have been there for a longer time. Contrary to previous understanding, our data clearly suggests that there is a turnover mechanism for the nuclear pore in non-dividing cells. This is exciting because it opens new research in this direction.

    Why is understanding protein turnover important?

    Protein turnover is important for understanding cellular homeostasis. Our work delineates the tools to study the mechanisms controlling it and will help researchers study a wide range of things, such as ageing, brain function, cancer and neurodegeneration.

    Science paper:
    Systematic analysis of protein turnover in primary cells, Nature Communications.

    See the full article here .

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    EMBL European Molecular Biology Laboratory campus

    EMBL is Europe’s flagship laboratory for the life sciences, with more than 80 independent groups covering the spectrum of molecular biology. EMBL is international, innovative and interdisciplinary – its 1800 employees, from many nations, operate across five sites: the main laboratory in Heidelberg, and outstations in Grenoble; Hamburg; Hinxton, near Cambridge (the European Bioinformatics Institute), and Monterotondo, near Rome. Founded in 1974, EMBL is an inter-governmental organisation funded by public research monies from its member states. The cornerstones of EMBL’s mission are: to perform basic research in molecular biology; to train scientists, students and visitors at all levels; to offer vital services to scientists in the member states; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research. Around 200 students are enrolled in EMBL’s International PhD programme. Additionally, the Laboratory offers a platform for dialogue with the general public through various science communication activities such as lecture series, visitor programmes and the dissemination of scientific achievements.

     
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