People’s ability to regenerate bones declines with age and is further decreased by diseases such as osteoporosis. To help the aging population, researchers are looking for new therapies that improve bone regeneration. Now, an interdisciplinary team of researchers from the Biotechnology Center (BIOTEC) and the Medical Faculty of TU Dresden, one of the 10 largest universities in Germany, along with a group from Max Bergmann Center of Biomaterials (MBC) developed novel bio-inspired molecules that enhance bone regeneration in mice. The results were published in the journal Biomaterials.

As people age, their ability to regenerate bones decreases. Fractures take longer to heal and diseases like osteoporosis only add to it. This represents a serious health challenge to the aging population and an increasing socioeconomic burden for society. To help combat this issue, researchers are looking for new therapeutic approaches that can improve bone regeneration.

A team of scientists from Dresden used supercomputer modeling and simulations to design novel bio-inspired molecules to enhance bone regeneration in mice. The new molecules can be incorporated into biomaterials and applied locally to bone defects. These new molecules are based on glycosaminoglycans, which are long-chained sugars such as hyaluronic acid or heparin.

A Sweet Solution for an Old Bone

“Thanks to our group’s work and the work of other researchers, we know a distinct molecular pathway that regulates bone formation and repair. In fact, we can narrow it down to two proteins that work together to block bone regeneration, sclerostin, and dickkopf-1” explains Prof. Lorenz Hofbauer, “The big challenge for developing drugs that improve bone healing is to efficiently turn off both of these proteins, which act as brake signals, at the same time.”

An interdisciplinary approach was key to this challenge. The Structural Bioinformatics group led by Prof. Maria Teresa Pisabarro at the Biotechnology Center (BIOTEC) of TU Dresden and the Functional Biomaterials group led by PD Dr. Vera Hintze at the Max Bergmann Center of Biomaterials (MBC), Institute of Materials Science of TU Dresden combined their know-how with bone expert Prof. Lorenz Hofbauer at the Medical Faculty of TU Dresden.

“For several years, we have harnessed the power of computer simulations to investigate how proteins regulating bone formation interact with their receptors. All this is to design new molecules that can efficiently interfere with these interactions. We worked in tandem between the computer and the bench, designing new molecules and testing them, feeding the results back to our molecular models and learning more about the molecular properties required for our goal,” explains Prof. Pisabarro.

Finally, the team of Lorenz Hofbauer’s Bone Lab used a biomaterial loaded with new molecules on bone defects in mice to test their effectiveness. The group found that materials containing the novel molecules outperformed the standard biomaterial and enhanced bone healing by up to 50%, which indicates their potential for improving bone regeneration.

Value-Added Chain: From Computer to the Lab Bench and Back

The multidisciplinary team used rational drug design to create novel molecules with tailored properties and minimal side effects. By using computational methods to predict and refine the properties of the designed molecules, the team was able to develop a series of candidates with the greatest potential for turning off the proteins that block bone regeneration.

Pisabarro group’s expertise allowed the thorough analysis of the three-dimensional (3D) structures of the two proteins that block bone regeneration. With that, they were able to model their interaction with their receptors in 3D and identify so-called hot spots, i.e., specific physicochemical and dynamic properties that are essential for the biological interaction to occur.

“We used molecular modeling to design new structures that mimic relevant receptor interactions with both proteins. We wanted this binding to be stronger than their natural interactions. In this way, our novel molecules would simultaneously hijack the proteins and effectively turn them off to turn the bone regeneration on,” explains Prof. Pisabarro.

“The molecules designed by Pisabarro’s group were synthesized by our colleagues at the Free University of Berlin and then analyzed regarding their protein binding properties via biophysical interaction analysis,” says PD Dr. Hintze. “For each of the molecules we were able to measure the binding strength with the proteins and their interference with natural receptor binding of the proteins. Thus, we could reveal empirically how effective each of the small molecules could be at turning off the inhibitory proteins.” Hofbauer group then tested the biological relevance of these interaction studies in a cell culture model and later in mice.

The results of such iterative testing are a valuable asset that enhances the current molecular models of the Pisabarro group and can be used to guide the development of novel and better molecules in the future. Such an approach also ensures that animal research is minimized and enters the project only in its final phase.

On the Way to Drug Development

The team's findings represent an exciting step forward in preclinical development. The newly designed molecules could potentially be used to turn off the proteins that block bone regeneration and lead to the development of novel, more effective treatments for bone fractures and other bone-related conditions.

The team continues to work together. “We are applying for funding for a pre-clinical study that will further develop the molecules and biomaterial-based bone booster to lay the ground for studies in humans,” says Prof. Hofbauer.

Fostering an Interdisciplinary Environment

The research was supported by the German Research Foundation (DFG). The groups were part of the Transregio 67 research consortium “Functional biomaterials for controlling healing processes in bone and skin tissue – from material to clinic (Dresden/Leipzig—TRR67 subprojects A3, A7, A8, B2, and Z3)”. Over more than 12 years, the three partners have worked in cooperation with other groups in Germany to generate novel insight into molecular mechanics and develop techniques as well as the necessary know-how to improve delayed bone regeneration.

Read more https://www.supercomputingonline.com/gallery/first-image-of-a-black-hole-expelling-a-powerful-jet-esocast-260-light

For the first time, astronomers have observed, in the same image, the shadow of the black hole at the center of the galaxy Messier 87 (M87) and the powerful jet expelled from it. The observations were done in 2018 with telescopes from the Global Millimetre VLBI Array (GMVA), the Atacama Large Millimeter/submillimeter Array (ALMA), of which ESO is a partner, and the Greenland Telescope (GLT). Thanks to this new image, astronomers can better understand how black holes can launch such energetic jets. 

Most galaxies harbor a supermassive black hole at their center. While black holes are known for engulfing matter in their immediate vicinity, they can also launch powerful jets of matter that extend beyond the galaxies that they live in. Understanding how black holes create such enormous jets has been a long-standing problem in astronomy. “We know that jets are ejected from the region surrounding black holes,” says Ru-Sen Lu from the Shanghai Astronomical Observatory in China, “but we still do not fully understand how this actually happens. To study this directly we need to observe the origin of the jet as close as possible to the black hole.”

The new image published today shows precisely this for the first time: how the base of a jet connects with the matter swirling around a supermassive black hole. The target is the galaxy M87, located 55 million light-years away in our cosmic neighborhood, and home to a black hole 6.5 billion times more massive than the Sun. Previous observations had managed to separately image the region close to the black hole and the jet, but this is the first time both features have been observed together. “This new image completes the picture by showing the region around the black hole and the jet at the same time,” adds Jae-Young Kim from the Kyungpook National University in South Korea and the Max Planck Institute for Radio Astronomy in Germany.

The image was obtained with the GMVA, ALMA, and the GLT, forming a network of radio telescopes around the globe working together as a virtual Earth-sized telescope. Such a large network can discern very small details in the region around M87’s black hole.

The new image shows the jet emerging near the black hole, as well as what scientists call the shadow of the black hole. As matter orbits the black hole, it heats up and emits light. The black hole bends and captures some of this light, creating a ring-like structure around the black hole as seen from Earth. The darkness at the center of the ring is the black hole shadow, which was first imaged by the Event Horizon Telescope (EHT) in 2017. Both this new image and the EHT one combine data taken with several radio telescopes worldwide, but the image released today shows radio light emitted at a longer wavelength than the EHT one: 3.5 mm instead of 1.3 mm. “At this wavelength, we can see how the jet emerges from the ring of emission around the central supermassive black hole,” says Thomas Krichbaum of the Max Planck Institute for Radio Astronomy. 

The size of the ring observed by the GMVA network is roughly 50% larger than the Event Horizon Telescope image. "To understand the physical origin of the bigger and thicker ring, we had to use computer simulations to test different scenarios,” explains Keiichi Asada from the Academia Sinica in Taiwan. The results suggest the new image reveals more of the material that is falling toward the black hole than what could be observed with the EHT. 

These new observations of M87’s black hole were conducted in 2018 with the GMVA, which consists of 14 radio telescopes in Europe and North America [1]. In addition, two other facilities were linked to the GMVA: the Greenland Telescope and ALMA, of which ESO is a partner. ALMA consists of 66 antennas in the Chilean Atacama desert, which played a key role in these observations. The data collected by all these telescopes worldwide are combined using a technique called interferometry, which synchronizes the signals taken by each individual facility. But to properly capture the actual shape of an astronomical object the telescopes must be spread all over the Earth. The GMVA telescopes are mostly aligned East-to-West, so the addition of ALMA in the Southern hemisphere proved essential to capture this image of the jet and shadow of M87’s black hole. “Thanks to ALMA’s location and sensitivity, we could reveal the black hole shadow and see deeper into the emission of the jet at the same time,” explains Lu.

Future observations with this network of telescopes will continue to unravel how supermassive black holes can launch powerful jets. “We plan to observe the region around the black hole at the center of M87 at different radio wavelengths to further study the emission of the jet,” says Eduardo Ros from the Max Planck Institute for Radio Astronomy. Such simultaneous observations would allow the team to disentangle the complicated processes that happen near the supermassive black hole. “The coming years will be exciting, as we will be able to learn more about what happens near one of the most mysterious regions in the Universe,” concludes Ros.

A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions.

Maybe you can’t tell a book from its cover, but according to researchers at MIT, you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what’s happening inside simply by observing the properties of the material’s surface.

The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure and used that to generate a system that could make reliable predictions of the interior from the surface data.

The results are being published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

“It’s a very common problem in engineering,” Buehler explains. “If you have a piece of material — maybe it’s a door on a car or a piece of an airplane — and you want to know what’s inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. But you can’t really look inside the material. The only way you can do that is by cutting it and then looking inside and seeing if there’s any kind of damage in there.”

It's also possible to use X-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. “So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what’s going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what’s actually going on inside?” That inside information might include any damages, cracks, or stresses in the material or details of its internal microstructure.

The same kind of questions can apply to biological tissues as well, he adds. “Is there disease in there, or some kind of growth or changes in the tissue?” The aim was to develop a system that could answer these kinds of questions in a completely non-invasive way.

Achieving that goal involved addressing complexities including the fact that “many such problems have multiple solutions,” Buehler says. For example, many different internal configurations might exhibit the same surface properties. To deal with that ambiguity, “we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular [surface] scenario.”

The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. This included not only uniform materials but also ones with different materials in combination. “Some new airplanes are made out of composites, so they have deliberate designs of having different phases,” Buehler says. “And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances.”

The technique works even for materials whose complexity is not fully understood, he says. “With complex biological tissue, we don’t understand exactly how it behaves, but we can measure the behavior. We don’t have a theory for it, but if we have enough data collected, we can train the model.”

Yang says that the method they developed is broadly applicable. “It is not just limited to solid mechanics problems, but it can also be applied to different engineering disciplines, like fluid dynamics and other types,” Buehler adds that it can be applied to determining a variety of properties, not just stress and strain, but fluid fields or magnetic fields, for example, the magnetic fields inside a fusion reactor. It is “very universal, not just for different materials, but also for different disciplines.”

Yang says that he initially started thinking about this approach when he was studying data on a material where part of the imagery he was using was blurred, and he wondered how it might be possible to “fill in the blank” of the missing data in the blurred area. “How can we recover this missing information?” he wondered. Reading further, he found that this was an example of a widespread issue, known as the inverse problem, of trying to recover missing information.

Developing the method involved an iterative process, having the model make preliminary predictions, comparing that with actual data on the material in question, then fine-tuning the model further to match that information. The resulting model was tested against cases where materials are well enough understood to be able to calculate the true internal properties, and the new method’s predictions matched up well against those calculated properties.

The training data included imagery of the surfaces, but also various other kinds of measurements of surface properties, including stresses, and electric and magnetic fields. In many cases, the researchers used simulated data based on an understanding of the underlying structure of a given material. And even when a new material has many unknown characteristics, the method can still generate an approximation that’s good enough to provide guidance to engineers with a general direction as to how to pursue further measurements.

As an example of how this methodology could be applied, Buehler points out that today, airplanes are often inspected by testing a few representative areas with expensive methods such as X-rays because it would be impractical to test the entire plane. “This is a different approach, where you have a much less expensive way of collecting data and making predictions,” Buehler says. “From that you can then make decisions about where do you want to look, and maybe use more expensive equipment to test it.”

To begin with, he expects this method, which is being made freely available for anyone to use through the website GitHub, to be mostly applied in laboratory settings, for example in testing materials used for soft robotics applications.

For such materials, he says, “We can measure things on the surface, but we have no idea what’s going on a lot of times inside the material, because it’s made out of a hydrogel or proteins or biomaterials for actuators, and there’s no theory for that. So, that’s an area where researchers could use our technique to make predictions about what’s going on inside, and perhaps design better grippers or better composites,” he adds.

The research was supported by the U.S. Army Research Office, the Air Force Office of Scientific Research, the GoogleCloud platform, and the MIT Quest for Intelligence.