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Cell-phone-radiation study finds associated brain and heart tumors in rodents

Glioma in rat brain (credit: Samuel Samnick et al./European Journal of Nuclear Medicine)

A series of studies over two years with rodents exposed to radio frequency radiation (RFR) found low incidences of malignant gliomas (tumors of glial support cells) in the brain and schwannoma tumors in the heart.*

The studies were performed under the auspices of the U.S. National Toxicology Program (NTP).

Potentially preneoplastic (pre-cancer) lesions were also observed in the brain and heart of male rats exposed to RFR, with higher confidence in the association with neoplastic lesions in the heart than the brain.

No biologically significant effects were observed in the brain or heart of female rats regardless of type of radiation.

The NTP notes that the open-access report is a preview and has not been peer-reviewed.**

In 2011, the WHO/International Agency for Research on Cancer (IARC) classified RFR as possibly carcinogenic to humans, also based on increased risk for glioma.

* The rodents were subjected to whole-body exposure to the two types RFR modulation currently used in U.S. wireless networks — CDMA and GSM — at frequencies of 900 MHz for rats and 1900 MHz for mice, with a total exposure time of approximately 9 hours a day over the course of the day, 7 days/week. The glioma lesions occurred in 2 to 3 percent of the rats and the schwannomas occurred in 1 to 6 percent of the rats.

** The NTP says further details will be published in the peer-reviewed literature later in 2016. The reports are “limited to select findings of concern in the brain and heart and do not represent a complete reporting of all findings from these studies of cell phone RFR,” which will be “reported together with the current findings in two forthcoming NTP peer-reviewed reports, to be available for peer review and public comment by the end of 2017.”

Abstract of Report of Partial Findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation

The U.S. National Toxicology Program (NTP) has carried out extensive rodent toxicology and carcinogenesis studies of radiofrequency radiation (RFR) at frequencies and modulations used in the US telecommunications industry. This report presents partial findings from these studies. The occurrences of two tumor types in male Harlan Sprague Dawley rats exposed to RFR, malignant gliomas in the brain and schwannomas of the heart, were considered of particular interest, and are the subject of this report. The findings in this report were reviewed by expert peer reviewers selected by the NTP and National Institutes of Health (NIH). These reviews and responses to comments are included as appendices to this report, and revisions to the current document have incorporated and addressed these comments. Supplemental information in the form of 4 additional manuscripts has or will soon be submitted for publication. These manuscripts describe in detail the designs and performance of the RFR exposure system, the dosimetry of RFR exposures in rats and mice, the results to a series of pilot studies establishing the ability of the animals to thermoregulate during RFR exposures, and studies of DNA damage. Capstick M, Kuster N, Kühn S, Berdinas-Torres V, Wilson P, Ladbury J, Koepke G, McCormick D, Gauger J, Melnick R. A radio frequency radiation reverberation chamber exposure system for rodents Yijian G, Capstick M, McCormick D, Gauger J, Horn T, Wilson P, Melnick RL and Kuster N. Life time dosimetric assessment for mice and rats exposed to cell phone radiation Wyde ME, Horn TL, Capstick M, Ladbury J, Koepke G, Wilson P, Stout MD, Kuster N, Melnick R, Bucher JR, and McCormick D. Pilot studies of the National Toxicology Program’s cell phone radiofrequency radiation reverberation chamber exposure system Smith-Roe SL, Wyde ME, Stout MD, Winters J, Hobbs CA, Shepard KG, Green A, Kissling GE, Tice RR, Bucher JR, Witt KL. Evaluation of the genotoxicity of cell phone radiofrequency radiation in male and female rats and mice following subchronic exposure.

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Automated top-down design technique simplifies creation of DNA origami nanostructures

The boldfaced line, known as a spanning tree, follows the desired geometric shape of the target DNA origami design method, touching each vertex just once. A spanning tree algorithm is used to map out the proper routing path for the DNA strand. (credit: Public Domain)

MIT, Baylor College of Medicine, and Arizona State University Biodesign Institute researchers have developed a radical new top-down DNA origami* design method based on a computer algorithm that allows for creating designs for DNA nanostructures by simply inputting a target shape.

DNA origami (using DNA to design and build geometric structures) has already proven wildly successful in creating myriad forms in 2- and 3- dimensions, which conveniently self-assemble when the designed DNA sequences are mixed together. The tricky part is preparing the proper DNA sequence and routing design for scaffolding and staple strands to achieve the desired target structure. Typically, this is painstaking work that must be carried out manually.

The new algorithm, which is reported together with a novel synthesis approach in the journal Science, promises to eliminate all that and expands the range of possible applications of DNA origami in biomolecular science and nanotechnology. Think nanoparticles for drug delivery and cell targeting, nanoscale robots in medicine and industry, custom-tailored optical devices, and most interesting: DNA as a storage medium, offering retention times in the millions of years.**

Shape-shifting, top-down software

Unlike traditional DNA origami, in which the structure is built up manually by hand, the team’s radical top-down autonomous design method begins with an outline of the desired form and works backward in stages to define the required DNA sequence that will properly fold to form the finished product.

“The Science paper turns the problem around from one in which an expert designs the DNA needed to synthesize the object, to one in which the object itself is the starting point, with the DNA sequences that are needed automatically defined by the algorithm,” said Mark Bathe, an associate professor of biological engineering at MIT, who led the research. “Our hope is that this automation significantly broadens participation of others in the use of this powerful molecular design paradigm.”

The algorithm, which is known as DAEDALUS (DNA Origami Sequence Design Algorithm for User-defined Structures) after the Greek craftsman and artist who designed labyrinths that resemble origami’s complex scaffold structures, can build any type of 3-D shape, provided it has a closed surface. This can include shapes with one or more holes, such as a torus.

A simplified version of the  top-down procedure used to design scaffolded DNA origami nanostructures. It starts with a polygon corresponding to the target shape. Software translates a wireframe version of this structure into a plan for routing DNA scaffold and staple strands. That enables a 3D DNA-based atomic-level structural model that is then validated using 3D cryo-EM reconstruction. (credit: adapted from Biodesign Institute images)

With the new technique, the target geometric structure is first described in terms of a wire mesh made up of polyhedra, with a network of nodes and edges. A DNA scaffold using strands of custom length and sequence is generated, using a “spanning tree” algorithm — basically a map that will automatically guide the routing of the DNA scaffold strand through the entire origami structure, touching each vertex in the geometric form once. Complementary staple strands are then assigned and the final DNA structural model or nanoparticle self-assembles, and is then validated using 3D cryo-EM reconstruction.

The software allows for fabricating a variety of geometric DNA objects, including 35 polyhedral forms (Platonic, Archimedean, Johnson and Catalan solids), six asymmetric structures, and four polyhedra with nonspherical topology, using inverse design principles — no manual base-pair designs needed.

To test the method, simpler forms known as Platonic solids were first fabricated, followed by increasingly complex structures. These included objects with nonspherical topologies and unusual internal details, which had never been experimentally realized before. Further experiments confirmed that the DNA structures produced were potentially suitable for biological applications since they displayed long-term stability in serum and low-salt conditions.

Biological research uses

The research also paves the way for designing nanoscale systems mimicking the properties of viruses, photosynthetic organisms, and other sophisticated products of natural evolution. One such application is a scaffold for viral peptides and proteins for use as vaccines. The surface of the nanoparticles could be designed with any combination of peptides and proteins, located at any desired location on the structure, in order to mimic the way in which a virus appears to the body’s immune system.

The researchers demonstrated that the DNA nanoparticles are stable for more than six hours in serum, and are now attempting to increase their stability further.

The nanoparticles could also be used to encapsulate the CRISPR-Cas9 gene editing tool. The CRISPR-Cas9 tool has enormous potential in therapeutics, thanks to its ability to edit targeted genes. However, there is a significant need to develop techniques to package the tool and deliver it to specific cells within the body, Bathe says.

This is currently done using viruses, but these are limited in the size of package they can carry, restricting their use. The DNA nanoparticles, in contrast, are capable of carrying much larger gene packages and can easily be equipped with molecules that help target the right cells or tissue.

The most exciting aspect of the work, however, is that it should significantly broaden participation in the application of this technology, Bathe says, much like 3-D printing has done for complex 3-D geometric models at the macroscopic scale.

Hao Yan directs the Biodesign Center for Molecular Design and Biomimetics at Arizona State University and is the Milton D. Glick Distinguished Professor, College of Liberal Arts and Sciences, School of Molecular Sciences at ASU.

* DNA origami brings the ancient Japanese method of paper folding down to the molecular scale. The basics are simple: Take a length of single-stranded DNA and guide it into a desired shape, fastening the structure together using shorter “staple strands,” which bind in strategic places along the longer length of DNA. The method relies on the fact that DNA’s four nucleotide letters—A, T, C, & G stick together in a consistent manner — As always pairing with Ts and Cs with Gs.

The DNA molecule in its characteristic double stranded form is fairly stiff, compared with single-stranded DNA, which is flexible. For this reason, single stranded DNA makes for an ideal lace-like scaffold material. Further, its pairing properties are predictable and consistent (unlike RNA).

** A single gram of DNA can store about 700 terabytes of information — an amount equivalent to 14,000 50-gigabyte Blu-ray disks — and could potentially be operated with a fraction of the energy required for other information storage options.


Biodesign Institute at ASU | DNA Origami

Abstract of Designer nanoscale DNA assemblies programmed from the top down

Scaffolded DNA origami is a versatile means of synthesizing complex molecular architectures. However, the approach is limited by the need to forward-design specific Watson-Crick base-pairing manually for any given target structure. Here, we report a general, top-down strategy to design nearly arbitrary DNA architectures autonomously based only on target shape. Objects are represented as closed surfaces rendered as polyhedral networks of parallel DNA duplexes, which enables complete DNA scaffold routing with a spanning tree algorithm. The asymmetric polymerase chain reaction was applied to produce stable, monodisperse assemblies with custom scaffold length and sequence that are verified structurally in 3D to be high fidelity using single-particle cryo-electron microscopy. Their long-term stability in serum and low-salt buffer confirms their utility for biological as well as nonbiological applications.

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Deep learning applied to drug discovery and repurposing

Deep neural networks for drug discovery (credit: Insilico Medicine, Inc.)

Scientists from Insilico Medicine, Inc. have trained deep neural networks (DNNs) to predict the potential therapeutic uses of 678 drugs, using gene-expression data obtained from high-throughput experiments on human cell lines from Broad Institute’s LINCS databases and NIH MeSH databases.

The supervised deep-learning drug-discovery engine used the properties of small molecules, transcriptional data, and literature to predict efficacy, toxicity, tissue-specificity, and heterogeneity of response.

“We used LINCS data from Broad Institute to determine the effects on cell lines before and after incubation with compounds, co-author and research scientist Polina Mamoshina explained to KurzweilIAI.

“We used gene expression data of total mRNA from cell lines extracted and measured before incubation with compound X and after incubation with compound X to identify the response on a molecular level. The goal is to understand how gene expression (the transcriptome) will change after drug uptake. It is a differential value, so we need a reference (molecular state before incubation) to compare.”

The research is described in a paper in the upcoming issue of the journal Molecular Pharmaceutics.

Helping pharmas accelerate R&D

Alex Zhavoronkov, PhD, Insilico Medicine CEO, who coordinated the study, said the initial goal of their research was to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs. “In the process we came up with more than 800 strong hypotheses in oncology, cardiovascular, metabolic, and CNS spaces and started basic validation,” he said.

The team measured the “differential signaling pathway activation score for a large number of pathways to reduce the dimensionality of the data while retaining biological relevance.” They then used those scores to train the deep neural networks.*

“This study is a proof of concept that DNNs can be used to annotate drugs using transcriptional response signatures, but we took this concept to the next level,” said Alex Aliper, president of research, Insilico Medicine, Inc., lead author of the study.

Via Pharma.AI, a newly formed subsidiary of Insilico Medicine, “we developed a pipeline for in silico drug discovery — which has the potential to substantially accelerate the preclinical stage for almost any therapeutic — and came up with a broad list of predictions, with multiple in silico validation steps that, if validated in vitro and in vivo, can almost double the number of drugs in clinical practice.”

Despite the commercial orientation of the companies, the authors agreed not to file for intellectual property on these methods and to publish the proof of concept.

Deep-learning age biomarkers

According to Mamoshina, earlier this month, Insilico Medicine scientists published the first deep-learned biomarker of human age — aiming to predict the health status of the patient — in a paper titled “Deep biomarkers of human aging: Application of deep neural networks to biomarker development” by Putin et al, in Aging; and an overview of recent advances in deep learning in a paper titled “Applications of Deep Learning in Biomedicine” by Mamoshina et al., also in Molecular Pharmaceutics.

Insilico Medicine is located in the Emerging Technology Centers at Johns Hopkins University in Baltimore, Maryland, in collaboration with Datalytic Solutions and Mind Research Network.

* In this study, scientists used the perturbation samples of 678 drugs across A549, MCF-7 and PC-3 cell lines from the Library of Integrated Network-Based Cellular Signatures (LINCS) project developed by the National Institutes of Health (NIH) and linked those to 12 therapeutic use categories derived from MeSH (Medical Subject Headings) developed and maintained by the National Library of Medicine (NLM) of the NIH.

To train the DNN, scientists utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. Cross-validation experiments showed that DNNs achieve 54.6% accuracy in correctly predicting one out of 12 therapeutic classes for each drug.

One peculiar finding of this experiment was that a large number of drugs misclassified by the DNNs had dual use, suggesting possible application of DNN confusion matrices in drug repurposing.


FutureTechnologies Media Group | Video presentation Insilico medicine

Abstract of Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7 and PC-3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. When applied to normalized gene expression data for “landmark genes,” DNN showed cross-validation mean F1 scores of 0.397, 0.285 and 0.234 on 3-, 5- and 12-category classification problems, respectively. At the pathway level DNN performed best with cross-validation mean F1 scores of 0.701, 0.596 and 0.546 on the same tasks. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

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Triggering the protein that programs cancer cells to kill themselves


WEHI | Apoptosis

Researchers at the Walter and Eliza Hall Institute in Australia have discovered a new way to trigger cell death that could lead to drugs to treat cancer and autoimmune disease.

Programmed cell death (a.k.a. apoptosis) is a natural process that removes unwanted cells from the body. Failure of apoptosis can allow cancer cells to grow unchecked or immune cells to inappropriately attack the body.

The protein known as Bak is central to apoptosis. In healthy cells, Bak sits in an inert state but when a cell receives a signal to die, Bak transforms into a killer protein that destroys the cell.

Triggering the cancer-apoptosis trigger

Institute researchers Sweta Iyer, PhD, Ruth Kluck, PhD, and colleagues unexpectedly discovered that an antibody they had produced to study Bak actually bound to the Bak protein and triggered its activation. They hope to use this discovery to develop drugs that promote cell death.

The researchers used information about Bak’s three-dimensional structure to find out precisely how the antibody activated Bak. “It is well known that Bak can be activated by a class of proteins called ‘BH3-only proteins’ that bind to a groove on Bak. We were surprised to find that despite our antibody binding to a completely different site on Bak, it could still trigger activation,” Kluck said.  “The advantage of our antibody is that it can’t be ‘mopped up’ and neutralized by pro-survival proteins in the cell, potentially reducing the chance of drug resistance occurring.”

Drugs that target this new activation site could be useful in combination with other therapies that promote cell death by mimicking the BH3-only proteins. The researchers are now working with collaborators to develop their antibody into a drug that can access Bak inside cells.

Their findings have just been published in the open-access journal Nature Communications. The research was supported by the National Health and Medical Research Council, the Australian Research Council, the Victorian State Government Operational Infrastructure Support Scheme, and the Victorian Life Science Computation Initiative.

Abstract of Identification of an activation site in Bak and mitochondrial Bax triggered by antibodies

During apoptosis, Bak and Bax are activated by BH3-only proteins binding to the α2–α5 hydrophobic groove; Bax is also activated via a rear pocket. Here we report that antibodies can directly activate Bak and mitochondrial Bax by binding to the α1–α2 loop. A monoclonal antibody (clone 7D10) binds close to α1 in non-activated Bak to induce conformational change, oligomerization, and cytochrome c release. Anti-FLAG antibodies also activate Bak containing a FLAG epitope close to α1. An antibody (clone 3C10) to the Bax α1–α2 loop activates mitochondrial Bax, but blocks translocation of cytosolic Bax. Tethers within Bak show that 7D10 binding directly extricates α1; a structural model of the 7D10 Fab bound to Bak reveals the formation of a cavity under α1. Our identification of the α1–α2 loop as an activation site in Bak paves the way to develop intrabodies or small molecules that directly and selectively regulate these proteins.

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Diamonds closer to becoming ideal power semiconductors

A diode array bonded to a natural single crystalline diamond plate. Inset: deposited anode metal on top of doped silicon nanomembrane. (credit: Jung-Hun Seo)

Researchers have developed a new method for doping (integrating elements to change a semiconductor’s properties) single crystals of diamond with boron at relatively low temperatures, without degradation.

Diamonds have properties that could make them ideal semiconductors for power electronics. They can handle high voltages and power, and electrical currents also flow through diamonds quickly, meaning the material would make for energy-efficient devices. And they are thermally conductive, which means diamond-based devices would dissipate heat quickly and easily (no need for bulky, expensive cooling methods). However. diamond’s rigid crystalline structure makes doping difficult.*

Doping a diamond with boron

Zhengqiang (Jack) Ma, a University of Wisconsin-Madison electrical and computer engineering professor, and his colleagues describe a solution in the Journal of Applied Physics, from AIP Publishing.

They discovered that if you bond a single-crystal diamond with a piece of silicon doped with boron, and heat it to 800 degrees Celsius (low compared to conventional techniques), the boron atoms will migrate from the silicon to the diamond. It turns out that the boron-doped silicon has defects such as vacancies, where an atom is missing in the lattice structure. Carbon atoms from the diamond will fill those vacancies, leaving empty spots for boron atoms.

This technique also allows for selective doping, which means more control when making devices. You can choose where to dope a single-crystal diamond simply by bonding the silicon to that spot.

The new method currently only works for P-type doping, where the semiconductor is doped with an element that provides positive charge carriers (in this case, the absence of electrons, called holes). The researchers are already working on a simple device using P-type single-crystal diamond semiconductors.

But to make electronic devices like transistors, you need N-type doping, which gives the semiconductor negative charge carriers (electrons). And other barriers remain: diamond is expensive and single crystals are very small.

Still, Ma says, achieving P-type doping is an important step, and might inspire others to find solutions for the remaining challenges. Eventually, he said, single-crystal diamond could be useful everywhere — perfect, for instance, for controlling power in the electrical grid.

* Currently, you can dope diamond by coating the crystal with boron and heating it to 1450 degrees Celsius. But it’s difficult to remove the boron coating at the end. This method only works on diamonds consisting of multiple crystals stuck together. Because such polydiamonds have irregularities between the crystals, single crystals would be superior semiconductors. You can dope single crystals by injecting boron atoms while growing the crystals artificially. The problem is the process requires powerful microwaves that can degrade the quality of the crystal.

Abstract of Thermal diffusion boron doping of single-crystal natural diamond

With the best overall electronic and thermal properties, single crystal diamond (SCD) is the extreme wide bandgap material that is expected to revolutionize power electronics and radio-frequency electronics in the future. However, turning SCD into useful semiconductors requires overcoming doping challenges, as conventional substitutional doping techniques, such as thermal diffusion and ion implantation, are not easily applicable to SCD. Here we report a simple and easily accessible doping strategy demonstrating that electrically activated, substitutional doping in SCD without inducing graphitization transition or lattice damage can be readily realized with thermal diffusion at relatively low temperatures by using heavily dopedSi nanomembranes as a unique dopant carrying medium. Atomistic simulations elucidate a vacancyexchange boron doping mechanism that occurs at the bonded interface between Si and diamond. We further demonstrate selectively doped high voltage diodes and half-wave rectifier circuits using such dopedSCD. Our new doping strategy has established a reachable path toward using SCDs for future high voltage power conversion systems and for other novel diamond based electronic devices. The novel dopingmechanism may find its critical use in other wide bandgap semiconductors.

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Robots learn to cut through clutter

New software developed by Carnegie Mellon University helps mobile robots deal efficiently with clutter, whether it is in the back of a refrigerator or on the surface of the moon. (credit: Carnegie Mellon University Personal Robotics Lab)

Carnegie Mellon University roboticists have developed an algorithm that helps robots cope with a cluttered world.

Robots are adept at picking up an object in a specified place (such as in a factory assembly line) and putting it down at another specified place (known as “pick-and-place,” or P&P, processes). But homes and other planets, for example, are a special challenge for robots.

When a person reaches for a milk carton in a refrigerator, he doesn’t necessarily move every other item out of the way. Rather, a person might move an item or two, while shoving others out of the way as the carton is pulled out.

Robot creativity

Robot employs a “push and shove” method (credit: Jennifer E. King et al./Proceedings of IEEE International Conference on Robotics and Automation)

In tests, the new “push and shove” algorithm helped a robot deal efficiently with clutter, but surprisingly, it also revealed the robot’s creativity in solving problems.

“It was exploiting sort of superhuman capabilities,” Siddhartha Srinivasa, associate professor of robotics, said of his lab’s two-armed mobile robot, the Home Exploring Robot Butler, or HERB. “The robot’s wrist has a 270-degree range, which led to behaviors we didn’t expect. Sometimes, we’re blinded by our own anthropomorphism.”

In one case, the robot used the crook of its arm to cradle an object to be moved. “We never taught it that,” Srinivasa said.

K-Rex rover prototype (credit: NASA)

The new algorithm was also tested on NASA’s KRex robot, which is being designed to traverse the lunar surface. While HERB focused on clutter typical of a home, KRex used the software to find traversable paths across an obstacle-filled landscape while pushing an object.

A “rearrangement planner” automatically finds a balance between the two strategies (pick-and-place vs. push-and-shove), Srinivasa said, based on the robot’s progress on its task. The robot is programmed to understand the basic physics of its world, so it has some idea of what can be pushed, lifted, or stepped on. And it can be taught to pay attention to items that might be valuable or delicate.

They researchers presented their work last week (May 19) at the IEEE International Conference on Robotics and Automation in Stockholm, Sweden. NASA, the National Science Foundation, Toyota Motor Engineering and Manufacturing, and the Office of Naval Research supported this research.

Abstract of Rearrangement Planning Using Object-Centric and Robot-Centric Action Spaces

This paper addresses the problem of rearrangement planning, i.e. to find a feasible trajectory for a robot that must interact with multiple objects in order to achieve a goal. We propose a planner to solve the rearrangement planning problem by considering two different types of actions: robot-centric and object-centric. Object-centric actions guide the planner to perform specific actions on specific objects. Robot-centric actions move the robot without object relevant intent, easily allowing simultaneous object contact and whole arm interaction. We formulate a hybrid planner that uses both action types. We evaluate the planner on tasks for a mobile robot and a household manipulator.

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No longer ‘junk DNA’ — shedding light on the ‘dark matter’ of the genome

The LIGR-seq method for global-scale mapping of RNA-RNA interactions in vivo to reveal unexpected functions for uncharacterized RNAs that act via base-pairing interactions (credit: University of Toronto)

What used to be dismissed by many as “junk DNA” has now become vitally important, as accelerating genomic data points to the importance of non-coding RNAs (ncRNAs) — a genome’s messages that do not specifically code for proteins — in development and disease.

But our progress in understanding these molecules has been slow because of the lack of technologies that allow for systematic mapping of their functions.

Now, professor Benjamin Blencowe’s team at the University of Toronto’s Donnelly Centre has developed a method called “LIGR-seq” that enables scientists to explore in depth what ncRNAs do in human cells.

The study, described in Molecular Cell, was published on May 19, along with two other papers, in Molecular Cell and Cell, respectively, from Yue Wan’s group at the Genome Institute of Singapore and Howard Chang’s group at Stanford University in California, who developed similar methods to study RNAs in different organisms.

So what exactly do ncRNAs do?

mRNAs vs. ncRNAs (credit: Thomas Shafee/CC)

Of the 3 billion letters in the human genome, only two per cent make up the protein-coding genes. The genes are copied, or transcribed, into messenger RNA (mRNA) molecules, which provide templates for building proteins that do most of the work in the cell. Much of the remaining 98 per cent of the genome was initially considered by some as lacking in functional importance. However, large swaths of the non-coding genome — between half and three quarters of it — are also copied into RNA.

So then what might the resulting ncRNAs do? That depends on whom you ask. Some researchers believe that most ncRNAs have no function, that they are just a by-product of the genome’s powerful transcription machinery that makes mRNA. However, it is emerging that many ncRNAs do have important roles in gene regulation — some ncRNAs act as carriages for shuttling the mRNAs around the cell, or provide a scaffold for other proteins and RNAs to attach to and do their jobs.

But the majority of available data has trickled in piecemeal or through serendipitous discovery. And with emerging evidence that ncRNAs could drive disease progression, such as cancer metastasis, there was a great need for a technology that would allow a systematic functional analysis of ncRNAs.

“Up until now, with existing methods, you had to know what you are looking for because they all require you to have some information about the RNA of interest. The power of our method is that you don’t need to preselect your candidates; you can see what’s occurring globally in cells, and use that information to look at interesting things we have not seen before and how they are affecting biology,” says Eesha Sharma, a PhD candidate in Blencowe’s group who, along with postdoctoral fellow Tim Sterne-Weiler, co-developed the method.

A new ncRNA identification tool

The human RNA-RNA interactome, showing interactions detected by LIGR-seq (credit: University of Toronto)

The new ‘‘LIGation of interacting RNA and high-throughput sequencing’’ (LIGR-seq) tool captures interactions between different RNA molecules. When two RNA molecules have matching sequences — strings of letters copied from the DNA blueprint — they will stick together like Velcro. With LIGR-seq, the paired RNA structures are removed from cells and analyzed by state-of-the-art sequencing methods to precisely identify the RNAs that are stuck together.

“Most researchers in the life sciences agree that there’s an urgent need to understand what ncRNAs do. This technology will open the door to developing a new understanding of ncRNA function,” says Blencowe, who is also a professor in the Department of Molecular Genetics.

Not having to rely on pre-existing knowledge will boost the discovery of RNA pairs that have never been seen before. Scientists can also, for the first time, look at RNA interactions as they occur in living cells, in all their complexity, unlike in the juices of mashed up cells that they had to rely on before. This is a bit like moving on to explore marine biology from collecting shells on the beach to scuba-diving among the coral reefs, where the scope for discovery is so much bigger.

Actually, ncRNAs come in multiple flavors: there’s rRNA, tRNA, snRNA, snoRNA, piRNA, miRNA, and lncRNA, to name a few, where prefixes reflect the RNA’s place in the cell or some aspect of its function. But the truth is that no one really knows the extent to which these ncRNAs control what goes on in the cell, or how they do this.

Discoveries

Nonetheless, the new technology developed by Blencowe’s group has been able to pick up new interactions involving all classes of RNAs and has already revealed some unexpected findings.

The team discovered new roles for small nucleolar RNAs (snoRNAs), which normally guide chemical modifications of other ncRNAs. It turns out that some snoRNAs can also regulate stability of a set of protein-coding mRNAs. In this way, snoRNAs can also directly influence which proteins are made, as well as their abundance, adding a new level of control in cell biology.

And this is only the tip of the iceberg; the researchers plan to further develop and apply their technology to investigate the ncRNAs in different settings.

“We would like to understand how ncRNAs function during development. We are particularly interested in their role in the formation of neurons. But we will also use our method to discover and map changes in RNA-RNA interactions in the context of human diseases,” says Blencowe.

Abstract of Global Mapping of Human RNA-RNA Interactions

The majority of the human genome is transcribed into non-coding (nc)RNAs that lack known biological functions or else are only partially characterized. Numerous characterized ncRNAs function via base pairing with target RNA sequences to direct their biological activities, which include critical roles in RNA processing, modification, turnover, and translation. To define roles for ncRNAs, we have developed a method enabling the global-scale mapping of RNA-RNA duplexes crosslinked in vivo, “LIGation of interacting RNA followed by high-throughput sequencing” (LIGR-seq). Applying this method in human cells reveals a remarkable landscape of RNA-RNA interactions involving all major classes of ncRNA and mRNA. LIGR-seq data reveal unexpected interactions between small nucleolar (sno)RNAs and mRNAs, including those involving the orphan C/D box snoRNA, SNORD83B, that control steady-state levels of its target mRNAs. LIGR-seq thus represents a powerful approach for illuminating the functions of the myriad of uncharacterized RNAs that act via base-pairing interactions.

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Cancer-patient big data can save lives if shared globally

Data-sharing vision as facilitated by GA4GH through its working groups (credit: GA4GH)

Sharing genetic information from millions of cancer patients around the world could revolutionize cancer prevention and care, according to a paper in Nature Medicine by the Cancer Task Team of the Global Alliance for Genomics and Health (GA4GH).

Hospitals, laboratories and research facilities around the world hold huge amounts of this data from cancer patients, but it’s currently held in isolated “silos” that don’t talk to each other, according to GA4GH, a partnership between scientists, clinicians, patients, and the IT and Life Sciences industry, involving more than 400 organizations in over 40 countries. GA4GH intends to provide a common framework for the responsible, voluntary and secure sharing of patients’ clinical and genomic data.

A searchable global cancer database

“Imagine if we could create a searchable cancer database that allowed doctors to match patients from different parts of the world with suitable clinical trials,” said GA4GH co-chair professor Mark Lawler, a leading cancer expert from Queen’s University Belfast. “This genetic matchmaking approach would allow us to develop personalized treatments for each individual’s cancer, precisely targeting rogue cells and improving outcomes for patients.

“This data sharing presents logistical, technical, and ethical challenges. Our paper highlights these challenges and proposes potential solutions to allow the sharing of data in a timely, responsible and effective manner. We hope this blueprint will be adopted by researchers around the world and enable a unified global approach to unlocking the value of data for enhanced patient care.”

GA4GH acknowledges that there are security issues, and has created a Security Working Group and a policy paper that documents the standards and implementation practices for protecting the privacy and security of shared genomic and clinical data.

Examples of current initiatives for clinico-genomic data-sharing include the U.S.-based Precision Medicine Initiative and the UK’s 100,000 Genomes Project, both of which have cancer as a major focus.

Professor Lawler is funded by the Medical Research Council and Cancer Research UK.

Abstract of Facilitating a culture of responsible and effective sharing of cancer genome data

Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.

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Transistor-based biosensor detects molecules linked to cancer, Alzheimer’s, and Parkinson’s

An inexpensive portable biosensor developed by researchers at Brazil’s National Nanotechnology Laboratory (credit: LNNano)

A novel nanoscale organic transistor-based biosensor that can detect molecules associated with neurodegenerative diseases and some types of cancer has been developed by researchers at the National Nanotechnology Laboratory (LNNano) in Brazil.

The transistor, mounted on a glass slide, contains the reduced form of the peptide glutathione (GSH), which reacts in a specific way when it comes into contact with the enzyme glutathione S-transferase (GST), linked to Parkinson’s, Alzheimer’s and breast cancer, among other diseases.

Sensitive water-gated copper phthalocyanine (CuPc) thin-film transistor (credit: Rafael Furlan de Oliveira et al./Organic Electronics)

“The device can detect such molecules even when they’re present at very low levels in the examined material, thanks to its nanometric sensitivity,” explained Carlos Cesar Bof Bufon, Head of LNNano’s Functional Devices & Systems Lab (DSF).

Bufon said the system can be adapted to detect other substances by replacing the analytes (detection compounds). The team is working on paper-based biosensors to further lower the cost, improve portability, and facilitate fabrication and disposal.

The research is published in the journal Organic Electronics.

Abstract of Water-gated phthalocyanine transistors: Operation and transduction of the peptide–enzyme interaction

The use of aqueous solutions as the gate medium is an attractive strategy to obtain high charge carrier density (1012 cm−2) and low operational voltages (<1 V) in organic transistors. Additionally, it provides a simple and favorable architecture to couple both ionic and electronic domains in a single device, which is crucial for the development of novel technologies in bioelectronics. Here, we demonstrate the operation of transistors containing copper phthalocyanine (CuPc) thin-films gated with water and discuss the charge dynamics at the CuPc/water interface. Without the need for complex multilayer patterning, or the use of surface treatments, water-gated CuPc transistors exhibited low threshold (100 ± 20 mV) and working voltages (<1 V) compared to conventional CuPc transistors, along with similar charge carrier mobilities (1.2 ± 0.2) x 10−3 cm2 V−1 s−1. Several device characteristics such as moderate switching speeds and hysteresis, associated with high capacitances at low frequencies upon bias application (3.4–12 μF cm−2), indicate the occurrence of interfacial ion doping. Finally, water-gated CuPc OTFTs were employed in the transduction of the biospecific interaction between tripeptide reduced glutathione (GSH) and glutathione S-transferase (GST) enzyme, taking advantage of the device sensitivity and multiparametricity.

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Gene helps prevent heart attack, stroke; may also block effects of aging

An atherosclerotic lesion. Such lesions can rupture and cause heart attacks and strokes. (credit: UVA School of Medicine)

University of Virginia School of Medicine have discovered that a gene called Oct4 — which scientific dogma insists is inactive in adults — actually plays a vital role in preventing ruptured atherosclerotic plaques inside blood vessels, the underlying cause of most heart attacks and strokes.

The researchers found that Oct4 controls the conversion of smooth muscle cells into protective fibrous “caps” inside plaques, making the plaques less likely to rupture. They also discovered that the gene promotes many changes in gene expression that are beneficial in stabilizing the plaques. In addition, the researchers believe it may be possible to develop drugs or other therapeutic agents that target the Oct4 pathway as a way to reduce the incidence of heart attacks or stroke.

Could impact many human diseases, regenerative medicine

The researchers are also currently testing Oct4′s possible role in repairing cellular damage and healing wounds, which would make it useful for regenerative medicine.

Oct4 is one of the “stem cell pluripotency factors” described by Shinya Yamanaka, PhD, of Kyoto University, for which he received the 2012 Nobel Prize. His lab and many others have shown that artificial over-expression of Oct4 within somatic cells grown in a lab dish is essential for reprogramming these cells into induced pluripotential stem cells, which can then develop into any cell type in the body or even an entire organism.

“Finding a way to reactivate this pathway may have profound implications for health and aging,” said researcher Gary K. Owens, director of UVA’s Robert M. Berne Cardiovascular Research Center. “This could impact many human diseases and the field of regenerative medicine. [It may also] end up being the ‘fountain-of-youth gene,’ a way to revitalize old and worn-out cells.”

The discovery is described in a paper published online in Nature Medicine. The work was funded by the National Institutes of Health, the Russian Science Foundation, the Russian Federal Agency of Scientific Organization, and the U.S. Department of Defense.

Abstract of Activation of the pluripotency factor OCT4 in smooth muscle cells is atheroprotective

Although somatic cell activation of the embryonic stem cell (ESC) pluripotency factor OCT4 has been reported, this previous work has been controversial and has not demonstrated a functional role for OCT4 in somatic cells. Here we demonstrate that smooth muscle cell (SMC)-specific conditional knockout of Oct4 in Apoe−/− mice resulted in increased lesion size and changes in lesion composition that are consistent with decreased plaque stability, including a thinner fibrous cap, increased necrotic core area, and increased intraplaque hemorrhage. Results of SMC-lineage-tracing studies showed that these effects were probably the result of marked reductions in SMC numbers within lesions and SMC investment within the fibrous cap, which may result from impaired SMC migration. The reactivation of Oct4 within SMCs was associated with hydroxymethylation of the Oct4promoter and was hypoxia inducible factor-1α (HIF-1α, encoded by HIF1A) and Krüppel-like factor-4 (KLF4)-dependent. These results provide the first direct evidence that OCT4 has a functional role in somatic cells, and they highlight the potential role of OCT4 in normal and diseased somatic cells.