“The Chestnut-bellied Flycatcher is not as well-known as Darwin’s finches,” said lead author Leonardo Campagna, an evolutionary geneticist at the Cornell Lab of Ornithology. “But this complex of birds has also gone through many evolutionary changes, many of which involve changes in the coloration and patterning of their plumage.”

The scenario: A large population of chestnut-bellied birds lives on one of the bigger islands in the Pacific chain. From there, some birds established new populations on a couple of smaller islands. Over time, birds on the two smaller islands lost their chestnut bellies and became all black. But the birds on each island developed black plumage at different times, from different genetic mutations which moved rapidly through the small island populations. One of these mutations spread during the last 1,000 years — a mere blink in evolutionary time.

“Clearly there’s something advantageous about having all-black plumage,” said Campagna. “We’ve traced this trait back through time by sequencing the entire Chestnut-bellied Flycatcher genome for the first time. The two mutations that lead to black plumage appeared at different times, on different islands, and on different genes related to melanin pigment production. That level of convergence is wild!”

The various flycatcher populations are in the early stages of speciation — splitting off to form new species — but they have not yet diverged much genetically and they can interbreed. But they rarely do, producing a few hybrids. Field experiments have shown the chestnut-bellied birds and the all-black birds each react aggressively toward a perceived interloper with their own plumage color, but do not respond the same way to the members of their species with a different color.

And it turns out Mother Nature is not done tinkering with the flycatcher genome.

“We’re finding there’s a third melanic (all black) population of flycatchers among islands about 300 miles away from the original island,” said senior co-author Al Uy, a biology professor at the University of Rochester. “The mutation governing their plumage color is different yet again from those on the other two islands we studied.”

Uy has been studying the Solomon Islands flycatchers for about 15 years, aided by a trusted group of indigenous islanders he says have been “instrumental” in his work.

“I think the emerging pattern is that there’s something about small islands that’s favoring these all-black birds — in the more distant archipelago were melanism has evolved for the third time, we found that melanic and chestnut-bellied birds still coexist within each island but as islands get smaller, the frequency of melanic birds goes up.”

There are multiple theories about what’s driving the switch to back plumage, including female preference, the greater durability of black feathers, and even a possible link to genes that govern other advantageous behaviors.

The study authors include computer scientists Ziyi Mo and Adam Siepel from Cold Spring Harbor Laboratory who wrote the machine learning program that helped the researchers dig deeper into the past and measure mutation patterns in the flycatcher “family tree.”

“The use of machine learning is an exciting new development in the field of population genetics,” said Campagna. “We train the computer to recognize specific evolutionary patterns for when a particular genetic trait started, how strong natural or sexual selection was, and how quickly it moved through a population. We can then ask the trained algorithm to tell us the most likely scenario that generated the data that we observe in the present populations. It’s like going back in time.’

Story Source:

Materials provided by Cornell University. Note: Content may be edited for style and length.

“For the first time, we have identified a root cause of chronic heart failure following a heart attack,” Dharmakumar said.

Dharmakumar is executive director of IU’s Krannert Cardiovascular Research Center and associate director for research at the Cardiovascular Institute, a joint enterprise between IU School of Medicine and IU Health.

“While advances across populations have made survival after a heart attack possible for most, too many survivors suffer long-term complications like heart failure,” said Subha Raman, MD, who is physician director of the Cardiovascular Institute. “Dr. Dharmakumar’s breakthrough science illuminates who is at risk and why and points to an effective way to prevent these complications.”

The multi-million-dollar study, which involved collaborators from institutions in the United States and Canada, followed large animal models over six months. It found that in heart attacks that result in bleeding within the heart muscle — which is about half of them — scar tissue is slowly replaced by fat. Fatty tissue can’t push blood from the heart effectively, and this is what leads to heart failure and eventually to death in many survivors of hemorrhagic heart attacks, Dharmakumar said.

“Using noninvasive imaging, histology and molecular biology techniques, and various other technologies, we have shown that iron from red blood cells is what drives this process,” he explained. “When we removed the iron, we reduced the amount of fat in the heart muscle. This finding establishes a pathway for clinical investigations to remedy or mitigate the effects associated with iron in hemorrhagic myocardial infarction patients.”

Dharmakumar’s team is currently testing iron chelation therapy to do just that in a just-launched clinical trial.

“Thanks to a clinical trial underway being led by his team at Indiana University, I’m excited to see this treatment improve the lives of millions of heart attack survivors worldwide,” said Raman.

Story Source:

Materials provided by Indiana University School of Medicine. Note: Content may be edited for style and length.

This technique could be used to finely tune the production of useful proteins, such as the monoclonal antibodies used to treat cancer and other diseases, or other aspects of cellular behavior. In their new study, which appears in Nature Communications, the researchers showed that this system can work in a variety of mammalian cells, with very consistent results.

“It’s a highly predictable system that we can design up front and then get the expected outcome,” says William C.W. Chen, a former MIT research scientist. “It’s a very tunable system and suitable for many different biomedical applications in different cell types.”

Chen, who is now an assistant professor of biomedical sciences at the University of South Dakota, is one of the lead authors of the new study, along with former MIT Research Scientist Leonid Gaidukov and postdoc Yong Lai. Senior author Timothy Lu led the research as an MIT associate professor of biological engineering and of electrical engineering and computer science.

Gene control

Many therapeutic proteins, including monoclonal antibodies, are produced in large bioreactors containing mammalian cells that are engineered to generate the desired protein. Several years ago, researchers in MIT’s Synthetic Biology Center, including Lu’s lab, began working with Pfizer Inc. on a project to develop synthetic biology tools that could be used to boost the production of these useful proteins.

To do so, the researchers targeted the promoters of the genes they wanted to upregulate. In all mammalian cells, genes have a promoter region that binds to transcription factors — proteins that initiate the transcription of the gene into messenger RNA.

In previous work, scientists have designed synthetic transcription factors, including proteins called zinc fingers, to help activate target genes. However, zinc fingers and most other types of synthetic transcription factors have to be redesigned for each gene that they target, making them challenging and time-consuming to develop.

In 2013, researchers in Lu’s lab developed a CRISPR-based transcription factor that allowed them to more easily control transcription of naturally occurring genes in mammalian and yeast cells. In the new study, the researchers set out to build on that work to create a library of synthetic biological parts that would allow them to deliver a transgene — a gene not normally expressed by the cell — and precisely control its expression.

“The idea is to have a full-spectrum synthetic promoter system that can go from very low to very high, to accommodate different cellular applications,” Chen says.

The system that the researchers designed includes several components. One is the gene to be transcribed, along with an “operator” sequence, which consists of a series of artificial transcription factor binding sites. Another component is a guide RNA that binds to those operator sequences. Lastly, the system also includes a transcription activation domain attached to a deactivated Cas9 protein. When this deactivated Cas9 protein binds to the guide RNA at the synthetic promoter site, the CRISPR-based transcription factor can turn on gene expression.

The promoter sites used for this synthetic system were designed to be distinct from naturally occurring promoter sites, so that the system won’t affect genes in the cells’ own genomes. Each operator includes between two and 16 copies of the guide RNA binding site, and the researchers found that their system could initiate gene transcription at rates that linearly correspond to the number of binding sites, allowing them to precisely control the amount of the protein produced.

High consistency

The researchers tested their system in several types of mammalian cells, including Chinese hamster ovary (CHO) cells, which are commonly used to produce therapeutic proteins in industrial bioreactors. They found very similar results in CHO cells and the other cells they tested, including mouse and rat myoblasts (precursors to muscle cells), human embryonic kidney cells, and human induced pluripotent stem cells.

“The system has very high consistency over different cell types and different target genes,” Chen says. “This is a good starting point for thinking about regulating gene expression and cell behavior with a highly tunable, predictable artificial system.”

After first demonstrating that they could use the new system to induce cells to produce expected amounts of fluorescent proteins, the researchers showed they could also use it to program the production of the two major segments of a monoclonal antibody known as JUG444.

The researchers also programmed CHO cells to produce different quantities of a human antibody called anti-PD1. When human T cells were exposed to these cells, they became more potent tumor cell killers if there was a larger amount of the antibody produced.

Although the researchers were able to obtain a high yield of the desired antibodies, further work would be needed to incorporate this system into industrial processes, they say. Unlike the cells used in industrial bioreactors, the cells used in this study were grown on a flat surface, rather than in a liquid suspension.

“This is a system that is promising to be used in industrial applications, but first we have to adapt this into suspended cells, to see if they make the proteins the same way. I suspect it should be the same, because there’s no reason that it shouldn’t, but we still need to test it,” Chen says.

The research was funded by the Pfizer-MIT RCA Synthetic Biology Program, the National Science Foundation, the National Institutes of Health, the University of South Dakota Sanford School of Medicine, an NIH Ruth L. Kirschstein NRSA postdoctoral fellowship, and the U.S. Department of Defense.

Cold Spring Harbor Laboratory (CSHL) Professor and HHMI Investigator Zachary Lippman is working to change that. Alongside CSHL and HHMI postdoc Jia He, Lippman built genetic blueprints, or models, for two types of groundcherry. These new models can guide plant geneticists toward unlocking groundcherries’ untapped potential. They may also be key in scaling up other nightshade plants for widespread use. Lippman says:

“The nightshade family encompasses more than 20 crops. There’s the major crops — potato, tomato, eggplant — and then there are minor crops, or orphan crops, which are either semi-domesticated or simply wild. A lot of those have not received a lot of research attention, but might have more potential for greater production if they could be developed further.”

Groundcherries are ideal models of the nightshade family because of their similar genetics and long evolutionary history. They’re also common in North America, easy to grow, and easy to genetically modify. But their most interesting attribute, Lippman says, might be the papery, balloon-like husk, or inflated calyx, that surrounds their berries.

“It seems to have evolved independently multiple times in flowering plants,” Lippman explains. “It’s not clear whether it’s a quirk of evolution, an adaptive trait, or both. But one thing, in my mind, is very clear — it’s one of the coolest evolutionary novelties to emerge in plants.”

Previous research on nightshades suggested genes called MADS-box were responsible for the inflated calyx’s emergence. Using the genome editing tool CRISPR on their new groundcherry models, Lippman and He switched off the MADS-box genes one by one. They found that the plants still grew an inflated calyx whether they had the genes or not. The models also allowed them to uncover a gene essential for inflated calyx development. He says:

“Without these genomic resources, it’s very hard to pinpoint the molecular mechanisms underlying some of the traits that arose through evolution. We think with our new resource, our new genomes, we can dissect this whole process.”

Lippman calls the new groundcherry models “the poster children” for what’s coming next from his lab. Their goal is to pepper the nightshade family with many new plant models, which will make it easier to improve crops through genome editing. These models will also help provide a better understanding of plants’ evolutionary development.

“Beyond improving crops, these new models can give us the means to answer the fundamental question of how new traits emerge in plants,” Lippman says. “I think Jia’s going to be the one to tackle that going forward.”

Story Source:

Materials provided by Cold Spring Harbor Laboratory. Original written by Nick Wurm. Note: Content may be edited for style and length.

The research, “Timing and duration of ephemeral Antarctic water tracks and wetlands using high temporal-resolution satellite imagery, high spatial-resolution satellite imagery, and ground-based sensors in the McMurdo Dry Valleys,” is published today in Arctic, Antarctic, and Alpine Research.

Researchers used newly available satellite imaging from Planet, which can scan the surface of the Earth daily or even multiple times per day. The new stream of data is having a major impact, as researchers in the past had to rely on far fewer satellite images, or had to make the voyage to Antarctica to conduct soil measurements in person.

“What we found was that when you can scan coastal Antarctica almost every day, not just a couple of times a summer, we see that the ground is actually thawing and turning into icy swamps a full month earlier than we used to think, and it’s staying wet and thawed a full two months later, even into March,” Said Levy.

“What’s even more fascinating is that this ground is thawing and staying thawed at temperatures below freezing, so we know salts must be helping it to melt and keeping it muddy, like salting a road during a snowstorm,” he said.

Levy explains that this extended melt is great for organisms that need meltwater to survive in the harsh climate, but it is bad news for the long-term stability of the permafrost. Co-authors of the paper include Levy’s former student, Lily Kuentz ’21, who is now studying at the University of Oregon, and Mark Salvatore of Northern Arizona University.Funded in part by a grant from the National Science Foundation, former Colgate students Kyle Baun ’21, Max Wang ’22, MC Rapoza ’22, Izzy King ’22, and Gary Kuang ’22 all contributed to the paper while they were completing their undergraduate degrees.

Story Source:

Materials provided by Colgate University. Original written by Daniel DeVries. Note: Content may be edited for style and length.

A team led by investigators at Massachusetts General Hospital (MGH) recently developed an artificial intelligence-based method to predict which patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. The method was validated in a study published in npj Precision Oncology.

Most patients with early-stage melanoma are treated with surgery to remove cancerous cells, but patients with more advanced cancer often receive immune checkpoint inhibitors, which effectively strengthen the immune response against tumor cells but also carry significant side effects.

“There is an urgent need to develop predictive tools to assist in the selection of high-risk patients for whom the benefits of immune checkpoint inhibitors would justify the high rate of morbid and potentially fatal immunologic adverse events observed with this therapeutic class,” says senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at MGH.

“Reliable prediction of melanoma recurrence can enable more precise treatment selection for immunotherapy, reduce progression to metastatic disease and improve melanoma survival while minimizing exposure to treatment toxicities.”

To help achieve this, Semenov and his colleagues assessed the effectiveness of algorithms based on machine learning, a branch of artificial intelligence, that used data from patient electronic health records to predict melanoma recurrence.

Specifically, the team collected 1,720 early-stage melanomas — 1,172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI) — and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients’ recurrence risk with machine learning algorithms. Algorithms were developed and validated with various MGB and DFCI patient sets, and tumor thickness and rate of cancer cell division were identified as the most predictive features.

“Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy,” says Semenov. “Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy.”

Additional Mass General co-authors include Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan Phillips, Nora A. Alexander, Yining Hua, Wenxin Chen, Diane, Ho, Stacey Duey, and Genevieve M. Boland.

This work was supported by the Melanoma Research Alliance, the National Institutes of Health, the Department of Defense, and the Dermatology Foundation.

Story Source:

Materials provided by Massachusetts General Hospital. Note: Content may be edited for style and length.