Human parainfluenza viruses, or HPIVs, are the leading cause of childhood respiratory infections, responsible for 30% to 40% of illnesses like croup and pneumonia. The viruses also affect the elderly and people with compromised immune systems.

To sicken people, HPIVs must latch onto cells and inject their genetic material to start making new viruses. HPIV3 is the most prevalent among these viruses. There are currently no approved vaccines or antivirals for HPIV3 infection in people.

In a study led by the Sam Gellman lab in the chemistry department at the University of Wisconsin-Madison, and the lab of Anne Moscona and Matteo Porotto at Columbia University, researchers built upon years of work on peptide treatments to generate one capable of blocking the HPIV3 attachment process.

The researchers published their findings April 7 in the Journal of the American Chemical Society.

To enter host cells, HPIVs use specialized fusion proteins that resemble three corkscrews laid side-by-side. Earlier work by the Moscona-Porotto lab showed that scientists could a partial chunk of this corkscrew protein from HPIV3, introduce this peptide to the virus, and prevent the corkscrew from driving the infection process. The peptide, itself a corkscrew, essentially zippers up with the virus’s corkscrews, creating a tight bundle of six corkscrew shapes.

The new peptide persists longer in the body, making it about three times more effective at blocking infection in rodent models of disease than the original form.

The research team started by trying to engineer the original peptide to be more resistant to protein-digesting enzymes in the body, which can easily shred proteins and make them useless. So, the Gellman lab turned to unusual building blocks to create a hardier peptide.

Cells build proteins out of alpha amino acids. But chemists can create beta amino acids, which are similar but have an extra carbon atom. When peptides use these beta amino acid building blocks, they often take a different shape because of the extra atom. This can help a peptide hide from protein-digesting enzymes and survive longer.

However, the researchers also knew that if the peptide’s shape changed too much as a result of these unusual building blocks, they might not lock together with HPIV’s corkscrew fusion protein.

That’s where the Gellman lab’s decades of experience testing and modifying beta amino acid-containing peptides became critical.

“We know which side of the peptide is binding to its protein target. So, we (knew that we) can modify only residues that are not directly involved in binding the viral protein,” says Victor Outlaw, a postdoctoral researcher in the lab and one of the co-first authors of the report. In lab tests, they saw that the carefully modified peptide still bound strongly to the virus’s protein.

In another improvement pioneered by the Moscona-Porotto lab, the scientists hooked the peptide up to a molecule of cholesterol. This fatty addition helps the peptide slide into the greasy cell membrane, where it can best block the virus.

“Our hypothesis was that the combination of beta amino acids and cholesterol would increase anti-viral efficacy,” says Outlaw, who explained that the cholesterol helped get the peptide to where it needed to go, while the shape change from the beta amino acids allowed the peptide to persist longer in the body.

As the research team hoped, when they gave the new peptide to cotton rats it lasted much longer in the lungs than the previous version did thanks to its resistance to digestion by enzymes. The peptide was delivered into the rats’ noses.

To test how well the peptide worked to prevent infection, cotton rats received the new peptide before they were exposed to HPIV3. Compared to animals given no antiviral peptides, those given the improved peptide had 10 times fewer viruses in their lungs.

And compared to the peptide that was more susceptible to enzymes, the hardier peptide reduced viral load by about three times, suggesting that the new peptide’s ability to avoid digestion in the body helps it better block infection.

While the approach hasn’t yet been tested in humans and researchers must further refine and test the system, it does provide a new strategy for potentially preventing or treating these common infections.

The research collaboration is now looking to make second-generation peptides that last even longer in the body. They also want to test how well the modified peptide might block infection by related viruses. That additional research could move the peptide treatment closer to clinical trials.

“This was a very fortunate coming together of groups that had complementary needs and capabilities,” says Gellman. “It’s really been a great joint effort.”

This work was supported by the National Institutes of Health (grants R01AI114736, R01 GM056414, F32 GM122263 and T32 GM008349.)

When taking a picture with a regular CMOS camera, like the ones on smartphones, the camera sensor is open to a large influx of photons during a predefined exposure time. Each pixel in the sensor grid outputs an analog value that depends on the number of photons that hit that pixel during exposure. However, this type of imaging has few ways to deal with moving objects; the movement of the object has to be much slower than the exposure time to avoid blurring. In contrast, single-photon cameras capture a rapid burst of consecutive frames with very short individual exposure times. These frames are binary — a grid of 1s and 0s that respectively indicate whether one photon arrived at each pixel or not during exposure. To reconstruct an actual picture from these binary frames (or bit planes), many of them have to be processed into a single non-binary image. This can be achieved by assigning different levels of brightness to all the pixels in the grid, depending on how many of the bit planes had a “1” for each pixel.

Besides its higher speed, the completely digital nature of single-photon imaging allows for designing clever image reconstruction algorithms that can make up for technical limitations or difficult scenarios. At Tokyo University of Science, Japan, Professor Takayuki Hamamoto has been leading a research team focused on taking the capabilities of single-photon imaging further. In the latest study by Prof. Hamamoto and his team, which was published in IEEE Access, they developed a highly effective algorithm to fix the blurring caused by motion in the imaged objects, as well as common blurring of the entire image such as that caused by the shaking of the camera.

Their approach addresses many limitations of existing deblurring techniques for single-photon imaging, which produce low-quality pictures when multiple objects in the scene are moving at different speeds and dynamically overlapping each other. Instead of adjusting the entire image according to the estimated motion of a single object or on the basis of spatial regions where the object is considered to be moving, the proposed method employs a more versatile strategy.

First, a motion estimation algorithm tracks the movement of individual pixels through statistical evaluations on how bit values change over time (over different bit planes). In this way, as demonstrated experimentally by the researchers, the motion of individual objects can be accurately estimated. “Our tests show that the proposed motion estimation technique produced results with errors of less than one pixel, even in dark conditions with few incident photons,” remarks Prof. Hamamoto.

The team then developed a deblurring algorithm that uses the results of the motion estimation step. This second algorithm groups pixels with a similar motion together, thereby identifying in each bit plane separate objects moving at different speeds. This allows for deblurring each region of the image independently according to the motions of objects that pass through it. Using simulations, the researchers showed that their strategy produced very crisp and high-quality images, even in low-light dynamic scenes crowded with objects coursing at disparate velocities.

Overall, the results of this study aptly showcase how greatly single-photon imaging can be improved if one gets down to developing effective image processing techniques. “Methods for obtaining crisp images in photon-limited situations would be useful in several fields, including medicine, security, and science. Our approach will hopefully lead to new technology for high-quality imaging in dark environments, like outer space, and super-slow recording that will far exceed the capabilities of today’s fastest cameras,” says Prof. Hamamoto. He also states that even consumer-level cameras might timely benefit from progress in single-photon imaging.

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Materials provided by Tokyo University of Science. Note: Content may be edited for style and length.

Now, EMBL scientists have combined artificial intelligence (AI) algorithms with two cutting-edge microscopy techniques — an advance that shortens the time for image processing from days to mere seconds, while ensuring that the resulting images are crisp and accurate. The findings are published in Nature Methods.

“Ultimately, we were able to take ‘the best of both worlds’ in this approach,” says Nils Wagner, one of the paper’s two lead authors and now a PhD student at the Technical University of Munich. “AI enabled us to combine different microscopy techniques, so that we could image as fast as light-field microscopy allows and get close to the image resolution of light-sheet microscopy.”

Although light-sheet microscopy and light-field microscopy sound similar, these techniques have different advantages and challenges. Light-field microscopy captures large 3D images that allow researchers to track and measure remarkably fine movements, such as a fish larva’s beating heart, at very high speeds. But this technique produces massive amounts of data, which can take days to process, and the final images usually lack resolution.

Light-sheet microscopy homes in on a single 2D plane of a given sample at one time, so researchers can image samples at higher resolution. Compared with light-field microscopy, light-sheet microscopy produces images that are quicker to process, but the data are not as comprehensive, since they only capture information from a single 2D plane at a time.

To take advantage of the benefits of each technique, EMBL researchers developed an approach that uses light-field microscopy to image large 3D samples and light-sheet microscopy to train the AI algorithms, which then create an accurate 3D picture of the sample.

“If you build algorithms that produce an image, you need to check that these algorithms are constructing the right image,” explains Anna Kreshuk, the EMBL group leader whose team brought machine learning expertise to the project. In the new study, the researchers used light-sheet microscopy to make sure the AI algorithms were working, Anna says. “This makes our research stand out from what has been done in the past.”

Robert Prevedel, the EMBL group leader whose group contributed the novel hybrid microscopy platform, notes that the real bottleneck in building better microscopes often isn’t optics technology, but computation. That’s why, back in 2018, he and Anna decided to join forces. “Our method will be really key for people who want to study how brains compute. Our method can image an entire brain of a fish larva, in real time,” Robert says.

He and Anna say this approach could potentially be modified to work with different types of microscopes too, eventually allowing biologists to look at dozens of different specimens and see much more, much faster. For example, it could help to find genes that are involved in heart development, or could measure the activity of thousands of neurons at the same time.

Next, the researchers plan to explore whether the method can be applied to larger species, including mammals.

A paper describing the study, led by Penn State Professor of Biology Hong Ma, appears in the May 2021 issue of the journal Molecular Plant.

“Legumes make up the third largest family of flowering plants and are incredibly diverse — ranging from tiny herbs to giant trees,” said Ma, who is the Huck Distinguished Research Professor of Plant Molecular Biology at Penn State. “They are essential food crops for both humans and livestock, can be used as lumber, and have many other uses. Maybe most importantly, they can ‘fix’ nitrogen — extracting the vital nutrient from the atmosphere and storing it in nodules on their roots in a symbiotic relationship with soil bacteria — making them important as green manure to improve soil health.”

There are over 19,000 species in the legume family divided into six subfamilies and then further divided into narrower and narrower groupings based on their evolutionary relationships. There are 765 genera — the grouping one level above species — of which the team sampled members of 333. To build the family tree, the team analyzed gene sequences from the transcriptomes — the portion of the genome that is expressed as genes — of most of the 463 species and a small number of shallowly sequenced whole genomes from across legume diversity.

“This is the largest study of this kind for a single plant family,” said Ma. “We went to great lengths to sample as many species as we could to get a broad representation of the legume family, but it is often difficult to get well-preserved specimens that we can extract DNA or RNA from, especially for species found in remote locations. Having this broad representation of species allowed us to build the most detailed nuclear-gene family tree for legumes to date.”

In addition to helping researchers understand the evolution and diversification of legumes, the new legume family tree helps to clarify the relationship between crop plants and their wild relatives. Although the close relatives of important agricultural crops are often known, studying more distant wild cousins could reveal traits that could be exploited to help plants thrive in changing environments and resist diseases or insect pests.

Across the legume family tree, the research team identified strong evidence for 28 separate whole-genome duplication events. Whole-genome duplications, evolutionary events that result in complete duplication of the entire genome, are fairly common among flowering plants and are thought to allow for functional innovation and evolutionary diversification. One of the duplication events that the team identified appears to have occurred in the ancestor of all members of the legume family.

“Because for most of the species in our study we used transcriptomes and do not have entire genome sequences, we consider these as ‘proposed’ genome duplication events,” said Ma. “These kinds of studies are kind of like solving a mystery. If you only have one or a few witnesses it might be difficult to convince a jury of your evidence, but if you have a hundred witnesses who have different perspectives and they all point to the same thing it becomes difficult to dismiss that evidence. In our case, the different species are like our witnesses. The size of our study allowed us to identify events that we might otherwise have dismissed.”

The two largest subfamilies account for over 17,000 legume species and include all of the species with the ability to fix nitrogen. Nitrogen is an important plant nutrient — most commercial fertilizers contain a mix of nitrogen, phosphorus and potassium — so the symbiotic relationship between some legumes and the microorganisms that allow them to assimilate nitrogen from the atmosphere using root nodules has spurred their success by allowing them to colonize areas with less fertile soil. The research team also identified clues to the evolution of the genes responsible for this important trait.

“Our data support the idea that nodulation and nitrogen fixation originated a single time early in the history of legumes and other related nitrogen-fixing plants and the whole-genome duplication event at the origin of legumes might have been crucial for the evolution of this process,” said Ma. “In addition to this duplication event, we are also able to see gene loss in plants that do not have the ability to nodulate, and evolutionary changes in genes that contributed to their role in nodulation.”

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Materials provided by Penn State. Original written by Sam Sholtis. Note: Content may be edited for style and length.