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.