Farmers could soon be growing tomatoes bunched like grapes in a storage unit, on the roof of a skyscraper, or even in space. That’s if a clutch of new gene-edited crops prove as fruitful as the first batch.
The primary goal of this new research is to engineer a wider variety of crops that can be grown in urban environments or other places not suitable for plant growth, said Cold Spring Harbor Laboratory Professor and HHMI Investigator Zach Lippman, who leads the lab that designed the ‘urban agriculture tomatoes.’
These new gene-edited tomato plants look nothing like the long vines you might find growing in a backyard garden or in agricultural fields. The most notable feature is their bunched, compact fruit. They resemble a bouquet whose roses have been replaced by ripe cherry tomatoes. They also mature quickly, producing ripe fruit that’s ready for harvest in under 40 days. And you can eat them.
“They have a great small shape and size, they taste good, but of course that all depends on personal preference,” Lippman said.
Most importantly, they’re eco-friendly.
“This demonstrates how we can produce crops in new ways, without having to tear up the land as much or add excessive fertilizer that runs off into rivers and streams,” Lippman said. “Here’s a complementary approach to help feed people, locally and with a reduced carbon footprint.”
That’s good news for anyone concerned about climate change. Earlier this year, the UN Intergovernmental Panel on Climate Change (IPCC) warned that more than 500 million people are living on land already degraded by deforestation, changing weather patterns, and overuse of viable cropland. By shifting some of the burden of growing the world’s crops to urban and other areas, there’s hope that desperate land mismanagement will slow.
Urban agricultural systems often call for compact plants that can be slotted or stacked into tight spaces, such as in tiered farming in warehouses or in converted storage containers. To make up for crop yield constrained by limited space, urban farms can operate year-round in climate-controlled conditions. That’s why it’s beneficial to use plants that can be grown and harvested quickly. More harvests per year results in more food, even if the space used is very small.
Lippman and his colleagues created the new tomatoes by fine-tuning two genes that control the switch to reproductive growth and plant size, the SELF PRUNING (SP) and SP5G genes, which caused the plant to stop growing sooner and flower and fruit earlier. But Lippman’s lab knew it could only modify the SP sister genes only so much before trading flavor or yield for even smaller plants.
“When you’re playing with plant maturation, you’re playing with the whole system, and that system includes the sugars, where they’re made, which is the leaves, and how they’re distributed, which is to the fruits,” Lippman said.
Searching for a third player, Lippman’s team recently discovered the gene SIER, which controls the lengths of stems. Mutating SIER with the CRISPR gene-editing tool and combining it with the mutations in the other two flowering genes created shorter stems and extremely compact plants.
Lippman is refining this technique, published in the latest issues of Nature Biotechnology, and hopes others will be inspired to try it on other fruit crops like kiwi. By making crops and harvests shorter, Lippman believes that agriculture can reach new heights.
“I can tell you that NASA scientists have expressed some interest in our new tomatoes,” he said.
While the first ship to Mars probably won’t have its own farm, astronauts may still get to test their green thumbs with urbanized, space-faring tomatoes.
The solar wind that pummels the Earth’s dayside magnetosphere causes turbulence, like air over a wing. Physicists at Rice University have developed new methods to characterize how that influences space weather on the nightside.
It’s rarely quiet up there. The solar wind streams around the Earth and cruises off into the night, but closer to the planet, parcels of plasma get caught in the turbulence and sink back toward Earth. That turbulence causes big ripples in the plasma.
With the help of several spacecraft and computational tools developed over the past decade, Rice scientists led by space plasma physicist Frank Toffoletto can now assess the ripples, called buoyancy waves, caused by the turbulence.
These waves, or oscillations, have been observed in the thin layer of magnetic flux along the base of the plasma sheet that tails away from the planet’s nightside. The Rice theory is the first to quantify their motion.
The theory adds another element to the Rice Convection Model, an established, decades-in-the-making algorithm that helps scientists calculate how the inner and middle magnetosphere will react to events like solar storms that threaten satellites, communications and power grids on Earth.
The new paper in JGR Space Physics by Toffoletto, emeritus professor Richard Wolf and former graduate student Aaron Schutza starts by describing the bubbles — “bursty bulk flows” predicted by Wolf and Rice alumnus Duane Pontius in 1990 — that fall back toward Earth through the plasma tail.
Functionally, they’re the reverse of buoyant air bubbles that bob up and down in the atmosphere because of gravity, but the plasma bubbles respond to magnetic fields instead. The plasma bubbles lose most of their momentum by the time they touch down at the theoretical, filamentlike boundary between the inner plasma sheet and the protective plasmasphere.
That sets the braking boundary into a gentle oscillation, which lasts mere minutes before stabilizing again. Toffoletto compared the motion to a plucked guitar string that quickly returns to equilibrium.
“The fancy name for this is the eigenmode,” he said. “We’re trying to figure out the low-frequency eigenmodes of the magnetosphere. They haven’t been studied very much, though they appear to be associated with dynamic disruptions to the magnetosphere.”
Toffoletto said the Rice team has in recent years discovered through simulations that the magnetosphere doesn’t always respond in a linear fashion to the steady driving force of the solar wind.
“You get all kinds of wave modes in the system,” he said, explaining that bursty bulk flows are one such mode. “Every time one of these things come flying in, when they hit the inner region, they basically reach their equilibrium point and oscillate with a certain frequency. Finding that frequency is what this paper is all about.”
As measured by the THE MIS spacecraft, the periods of these waves are a few minutes and the amplitudes are often bigger than the Earth.
“Understanding the natural frequency of the system and how it behaves can tell us a lot about the physical properties of plasma on the night side, its transport and how it might be related to the aurora,” he said. “A lot of these phenomena show up in the ionosphere as auroral structures, and we don’t understand where these structures come from.”
Toffoletto said the models suggest buoyant waves may play a role in the formation of the ring current that consists of charged particles that flow around Earth as well as magnetospheric substorms, all of which are connected to the aurora.
He said that no more than a decade ago, many magnetosphere simulations “would look very uniform, kind of boring.” The Rice group is collaborating with the Applied Physics Laboratory to include the Rice Convection Model in a newly developed global magnetosphere code called “Gamera,” named after the fictional Japanese monster.
“Now, with such higher-resolution models and much better numerical methods, these structures are starting to show up in the simulations,” Toffoletto said. “This paper is one little piece of the puzzle we’re putting together of how the system behaves. All this plays a big role in understanding how space weather works and how that in turn impacts technology, satellites and ground-based systems.”
The Rice Convection Model itself was refreshed this month in a paper led by recent Rice alumnus Jian Yang, now an associate professor of Earth and space sciences at the Southern University of Science and Technology, Shenzhen, China.
Lithium-ion batteries are notorious for developing internal electrical shorts that can ignite a battery’s liquid electrolytes, leading to explosions and fires. Engineers at the University of Illinois have developed a solid polymer-based electrolyte that can self-heal after damage — and the material can also be recycled without the use of harsh chemicals or high temperatures.
The new study, which could help manufacturers produce recyclable, self-healing commercial batteries, is published in the Journal of the American Chemical Society.
As lithium-ion batteries go through multiple cycles of charge and discharge, they develop tiny, branchlike structures of solid lithium called dendrites, the researchers said. These structures reduce battery life, cause hotspots and electrical shorts, and sometimes grow large enough to puncture the internal parts of the battery, causing explosive chemical reactions between the electrodes and electrolyte liquids.
There has been a push by chemists and engineers to replace the liquid electrolytes in lithium-ion batteries with solid materials such as ceramics or polymers, the researchers said. However, many of these materials are rigid and brittle resulting in poor electrolyte-to-electrode contact and reduced conductivity.
“Solid ion-conducting polymers are one option for developing nonliquid electrolytes,” said Brian Jing, a materials science and engineering graduate student and study co-author. “But the high-temperature conditions inside a battery can melt most polymers, again resulting in dendrites and failure.”
Past studies have produced solid electrolytes by using a network of polymer strands that are cross-linked to form a rubbery lithium conductor. This method delays the growth of dendrites; however, these materials are complex and cannot be recovered or healed after damage, Jing said.
To address this issue, the researchers developed a network polymer electrolyte in which the cross-link point can undergo exchange reactions and swap polymer strands. In contrast to linear polymers, these networks actually get stiffer upon heating, which can potentially minimize the dendrite problem, the researchers said. Additionally, they can be easily broken down and resolidified into a networked structure after damage, making them recyclable, and they restore conductivity after being damaged because they are self-healing.
“This new network polymer also shows the remarkable property that both conductivity and stiffness increase with heating, which is not seen in conventional polymer electrolytes,” Jing said.
“Most polymers require strong acids and high temperatures to break down,” said materials science and engineering professor and lead author Christopher Evans. “Our material dissolves in water at room temperature, making it a very energy-efficient and environmentally friendly process.”
The team probed the conductivity of the new material and found its potential as an effective battery electrolyte to be promising, the researchers said, but acknowledge that more work is required before it could be used in batteries that are comparable to what is in use today.
“I think this work presents an interesting platform for others to test,” Evans said. “We used a very specific chemistry and a very specific dynamic bond in our polymer, but we think this platform can be reconfigured to be used with many other chemistries to tweak the conductivity and mechanical properties.”
Stem cells located in the bone marrow generate and control the production of blood and immune cells. Researchers from EMBL, DKFZ and HI-STEM have now developed new methods to reveal the three-dimensional organization of the bone marrow at the single cell level. Using this approach the teams have identified previously unknown cell types that create specific local environments required for blood generation from stem cells. The study, published in Nature Cell Biology, reveals an unexpected complexity of the bone marrow and its microdomains at an unprecedented resolution and provides a novel scientific basis to study blood diseases such as leukemias.
In the published study researchers from European Molecular Biology Laboratory (EMBL), the German Cancer Research Center (DKFZ) and the Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH) present new methods permitting the characterisation of complex organs. The team focused their research on the murine bone marrow, as it harbours blood stem cells that are responsible for life-long blood production. Because of the ability to influence stem cells and to sustain blood production, there is a growing interest in exploiting the bone marrow environment, also called niche, as a target for novel leukemia treatments. “So far, very little was known about how different cells are organised within the bone marrow and how they interact to maintain blood stem cells,” explains Chiara Baccin, post-doc in the Steinmetz Group at EMBL. “Our approach unveils the cellular composition, the three-dimensional organisation and the intercellular communication in the bone marrow, a tissue that has thus far been difficult to study using conventional methods,” further explains Jude Al-Sabah, PhD student in the Haas Group at HI-STEM and DKFZ.
In order to understand which cells can be found in the bone marrow, where they are localised and how they might impact on stem cells, the researchers combined single-cell and spatial transcriptomics with novel computational methods. By analysing the RNA content of individual bone marrow cells, the team identified 32 different cell types, including extremely rare and previously unknown cell types. “We believe that these rare ‘niche cells’ establish unique environments in the bone marrow that are required for stem cell function and production of new blood and immune cells,” explains Simon Haas, group leader at the DKFZ and HI-STEM, and one of the initiators of the study.
Using novel computational methods, the researchers were not only able to determine the organisation of the different cell types in the bone marrow in 3D, but could also predict their cellular interactions and communication. “It’s the first evidence that spatial interactions in a tissue can be deduced computationally on the basis of genomic data,” explains Lars Velten, staff scientist in the Steinmetz Group.
“Our dataset is publicly accessible to any laboratory in the world and it could be instrumental in refining in vivo studies,” says Lars Steinmetz, group leader and director of the Life Science Alliance at EMBL Heidelberg. The data, which is now already used by different teams all over the world, is accessible via a user-friendly web app.
The developed methods can in principle be used to analyse the 3D organisation of any organ at the single cell level. “Our approach is widely applicable and could also be used to study the complex pathology of human diseases such as anemia or leukemia” highlights Andreas Trumpp, managing director of HI-STEM and division head at DKFZ.
In the last decade, scientists have made tremendous progress in understanding that groups of bacteria and viruses that naturally coexist throughout the human body play an important role in some vital functions like digestion, metabolism and even fighting off diseases. But understanding just how they do it remains a question.
Researchers from Drexel University are hoping to help answer that question through a clever combination of high-throughput genetic sequencing and natural language processing computer algorithms. Their research, which was recently published in the journal PLOS ONE, reports a new method of analyzing the codes found in RNA that can delineate human microbial communities and reveal how they operate.
Much of the research on the human microbial environment — or microbiome — has focused on identifying all of the different microbe species. And the nascent development of treatments for microbiota-linked maladies operates under the idea that imbalances or deviations in the microbiome are the source of health problems, such as indigestion or Crohn’s disease.
But to properly correct these imbalances it’s important for scientists to have a broader understanding of microbial communities as they exist — both in the afflicted areas and throughout the entire body.
“We are really just beginning to scrape the surface of understanding the health effects of microbiota,” said Gail Rosen, PhD, an associate professor in Drexel’s College of Engineering, who was an author of the paper. “In many ways scientists have jumped into this work without having a full picture of what these microbial communities look like, how prevalent they are and how their internal configuration affects their immediate environment within the human body.”
Rosen heads Drexel’s Center for Biological Discovery from Big Data, a group of researchers that has been applying algorithms and machine learning to help decipher massive amounts of genetic sequencing information that has become available in the last handful of years. Their work and similar efforts around the world have moved microbiology and genetics research from the wet lab to the data center — creating a computational approach to studying organism interactions and evolution, called metagenomics.
In this type of research, a scan of a genetic material sample — DNA or RNA — can be interpreted to reveal the organisms that are likely present. The method presented by Rosen’s group takes that one step farther by analyzing the genetic code to spot recurring patterns, an indication that certain groups of organisms — microbes in this case — are found together so frequently that it’s not a coincidence.
“We call this method ‘themetagenomics,’ because we are looking for recurring themes in microbiomes that are indicators of co-occurring groups of microbes,” Rosen said. “There are thousands of species of microbes living in the body, so if you think about all the permutations of groupings that could exist you can imagine what a daunting task it is to determine which of them are living in community with each other. Our method puts a pattern-spotting algorithm to work on the task, which saves a tremendous amount of time and eliminates some guesswork.”
Current methods for studying microbiota, gut bacteria for example, take a sample from an area of the body and then look at the genetic material that’s present. This process inherently lacks important context, according to the authors.
“It’s impossible to really understand what microbe communities are doing if we don’t first understand the extent of the community and how frequently and where else they might be occurring in the body,” said Steve Woloszynek, PhD, and MD trainee in Drexel’s College of Medicine and co-author of the paper. “In other words, it’s hard to develop treatments to promote natural microbial coexistence if their ‘natural state’ is not yet known.”
Obtaining a full map of microbial communities, using themetagenomics, allows researchers to observe how they change over time — both in healthy people and those suffering from diseases. And observing the difference between the two provides clues to the function of the community, as well as illuminating the configuration of microbe species that enables it.
“Most metagenomics methods just tell you which microbes are abundant — therefore likely important — but they don’t really tell you much about how each species is supporting other community members,” Rosen said. “With our method you get a picture of the configuration of the community — for example, it may have E. coli and B. fragilis as the most abundant microbes and in pretty equal numbers — which may indicate that they’re cross-feeding. Another community may have B. fragilis as the most abundant microbe, with many other microbes in equal, but lower, numbers — which could indicate that they are feeding off whatever B. fragilis is making, without any cooperation.”
One of the ultimate goals of analyzing human microbiota is to use the presence of certain microbe communities as indicators to identify diseases like Crohn’s or even specific types of cancer. To test their new method, the Drexel researchers put it up against similar topic modeling procedures that diagnose Crohn’s and mouth cancer by measuring the relative abundance of certain genetic sequences.
The themetagenomics method proved to be just as accurate predicting the diseases, but it does it much faster than the other topic modeling methods — minutes versus days — and it also teases out how each microbe species in the indicator community may contribute to the severity of the disease. With this level of granularity, researchers will be able to home in on particular genetic groupings when developing targeted treatments.
The group has made its themetagenomics analysis tools publicly available in hopes of speeding progress toward cures and treatments for these maladies.
“It’s very early right now, but the more that we understand about how the microbiome functions — even just knowing that groups may be acting together — then we can look into the metabolic pathways of these groups and intervene or control them, thus paving the way for drug development and therapy research,” Rosen said.
In what the authors believe is one of the first studies to examine climate change impact on the timing of bird migration on a continental scale, researchers report that spring migrants were likely to pass certain stops earlier now than they would have 20 years ago. Also, temperature and migration timing were closely aligned, with the greatest changes in migration timing occurring in the regions warming most rapidly. Timing shifts were less apparent in fall, they add.
Writing in Nature Climate Change, lead researcher Kyle Horton at Colorado State University (CSU), with artificial intelligence researcher Dan Sheldon at the University of Massachusetts Amherst and senior author Andrew Farnsworth of the Cornell Lab of Ornithology, describe how they analyzed 24 years of radar data from the National Oceanic and Atmospheric Administration (NOAA) for this study of nocturnal bird migration.
Horton describes the breadth of the research, which observed nighttime migratory behaviors of hundreds of species representing billions of birds, as “critically important” to understanding and learning more answers about shifting migration patterns. “To see changes in timing at continental scales is truly impressive, especially considering the diversity of behaviors and strategies used by the many species the radars capture,” he says, adding that the observed shifts do not necessarily mean that migrants are keeping pace with climate change.
Farnsworth says the team’s research answered, for the first time, key questions on birds and climate change. “Bird migration evolved largely as a response to changing climate,” he points out. “It’s a global phenomenon involving billions of birds annually. And it’s not a surprise that birds’ movements track changing climates. But how assemblages of bird populations respond in an era of such rapid and extreme changes in climate has been a black box. Capturing scales and magnitudes of migration in space and time has been impossible until recently.”
Horton says that this access to the data and cloud computing greatly enhanced the team’s ability to synthesize the findings. “To process all of these data, without cloud computing, it would have taken over a year of continuous computing,” he notes. Instead, the team crunched the numbers in about 48 hours.
As Sheldon at UMass Amherst points out, these bird flights have been recorded for decades by the National Weather Services’ network of constantly scanning weather radars, but until recently these data have been mostly out of reach for bird researchers, partly because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible.
For this study, Amazon Web Services provided access to the data. Also, a new tool, “MistNet,” developed by Sheldon and colleagues at UMass Amherst with others at the Cornell Lab uses machine learning to extract bird data from the radar record and to take advantage of the decades-long radar data archives. The name refers to the fine, almost invisible, “mist nets” that ornithologists use to capture migratory songbirds.
As Sheldon explains, MistNet automates the processing of a massive data set that has measured bird migration over the continental U.S. for over two decades, with excellent results when compared to humans working by hand. It uses computer vision techniques to differentiate birds from rain on the images, a major hurdle that had challenged biologists for decades. “Historically, a person had to look at each radar image to determine whether it contained rain or birds,” he notes. “We developed ‘MistNet,’ an artificial intelligence system to detect patterns in radar images and remove rain automatically.”
Sheldon’s group made earlier maps of where and when migration occurred over the past 24 years and animated these to illustrate, for example, the most intensive migration areas in the continental United States in a corridor roughly along and just west of the Mississippi River. MistNet also allows researchers to estimate flying velocity and traffic rates of migrating birds.
Horton at CSU says that the lack of change in fall migration patterns was a little surprising, though migration also tends to be a “little bit messier” during those months. “In the spring, we see bursts of migrants, moving at a fairly rapid pace, ultimately to reach the breeding grounds,” he explained. “However, during the fall, there’s not as much pressure to reach the wintering grounds, and migration tends to move at a slower, more punctuated pace.”
A combination of factors makes fall migration more challenging to study, he adds. In the fall, birds are not competing for mates and the pace to reach their destination is more relaxed. There’s also a wider age range of birds migrating, as the young eventually realize they need to migrate, too.
Horton said the findings have implications for understanding future patterns of bird migration, because the birds rely on food and other resources as they travel. Under climate change, the timing of blooming vegetation or emergence of insects may be out of sync with the passage of migratory birds. They say even subtle shifts could have negative consequences for the health of migratory birds. In the future, the researchers plan to expand their data analysis to include Alaska, where climate change is having more serious impacts than in the lower 48 states in the U.S.
Domesticated rice has fatter seed grains with higher starch content than its wild rice relatives — the result of many generations of preferential seed sorting and sowing. But even though rice was the first crop to be fully sequenced, scientists have only documented a few of the genetic changes that made rice into a staple food for more than half the world’s population.
New research now finds that a sizeable amount of domestication-related changes in rice reflects selection on traits that are determined by a portion of the genome that does not transcribe proteins.
Xiaoming Zheng, a biologist with the Institute of Crop Sciences at the Chinese Academy of Agricultural Sciences, is the first author of newly published paper in Science Advances, “Genome-wide analyses reveal the role of non-coding variation in complex traits during rice domestication.” Qingwen Yang and Jun Liu, also from the Institute of Crop Sciences in the Chinese Academy of Agricultural Sciences, and Kenneth M. Olsen from Washington University in St. Louis are also communicating authors of this paper.
Noncoding RNAs are suspected to play very important roles in regulating growth and development, but they’re only beginning to be characterized.
“Despite almost 20 years of genomics and genome-enabled studies of crop domestication, we still know remarkably little about the genetic basis of most domestication traits in most crop species,” said Olsen, professor of biology in Arts & Sciences at Washington University.
“Early studies tended to go for ‘low-hanging fruit’ — simple traits that were controlled by just one or two genes with easily identifiable mutations,” Olsen said. “Far more difficult is figuring out the more subtle developmental changes that were critical for a lot of the changes during crop domestication.
“This study offers a step in that direction, by examining one regulatory mechanism that has been critical for modulating domestication-associated changes in rice grain development.”
Diversity of traits
A large proportion of the DNA in the chromosomes of many plants and animals comprises genes that do not encode instructions for making proteins — up to 98% of the genome for any given species. But this genetic information is poorly understood. Some scientists have called this stuff the ‘dark matter’ of the genome, or even dismissed it as ‘junk DNA’ — but it appears to have played an out sized role in rice development.
In this study, researchers found that key changes that occurred during rice domestication more than 9,000 years ago could be tied back to molecules called long-noncoding RNAs (lnc RNAs), a class of RNA molecules with a length of more than 200 nucleotides.
About 36 percent of the genetic information recorded in the rice genome can be tracked back to non coding regions, but more than 50 percent of the diversity of traits important to agriculture is linked to these same areas, the researchers found.
“For the first time, the lnc RNAs in noncoding region of cultivated rice and wild rice was deeply annotated and described,” Zheng said.
“Our transgenic experiments and population genetic analysis convincingly demonstrate that selection on lnc RNAs contributed to changes in domesticated rice grain quality by altering the expression of genes that function in starch synthesis and grain pigmentation,” she said.
Working with several hundred rice samples and more than 260 Gbs of sequence, the researchers employed sensitive detection techniques to quantify and robustly track lnc RNA transcription in rice. The new study validates some previously identified lnc RNAs and also provides new information on previously un described molecules.
This new study adds fuel to speculation by some researchers that most adaptive differences between groups of plants or animals are due to changes in gene regulation, and not protein evolution.
“Based on our findings, we propose that selection on lnc RNAs could prove to be a broader mechanism by which genome-wide patterns of gene expression can evolve in many species,” Zheng said.
This rice study also opens eyes and possibly new doors for producing new crops and grains through precision breeding.