Thursday, April 04, 2024

FLORA

New sunflower family tree reveals multiple origins of flower symmetry




PENN STATE

Sunflower family tree 

IMAGE: 

A NEW SUNFLOWER FAMILY TREE REVEALS THAT FLOWER SYMMETRY EVOLVED MULTIPLE TIMES INDEPENDENTLY. SPECIES OF THE SUNFLOWER FAMILY WITH OR WITHOUT BILATERAL FLOWER SYMMETRY. CHRYSANTHEMUM LAVANDULIFOLIUM (UPPER LEFT) AND ARTEMISIA ANNUA (UPPER RIGHT) ARE CLOSELY RELATED SPECIES FROM THE SAME TRIBE; THE FORMER HAS BILATERALLY SYMMETRIC FLOWERS (THE RAYS) AND THE LATTER DOES NOT. RUDBECKIA HIRTA (LOWER LEFT) FROM THE SUNFLOWER TRIBE HAS BILATERALLY SYMMETRIC FLOWERS, AND EUPATORIUM CHINENSE (LOWER RIGHT) FROM THE EUPATORIEAE TRIBE DOES NOT; THESE TWO TRIBES ARE CLOSELY RELATED GROUPS. A SUNFLOWER (CENTER) SHOWS FLOWERS WITH BILATERAL SYMMETRY (THE LARGE PETAL-LIKE FLOWERS IN THE OUTER ROW) AND WITHOUT (THE SMALL FLOWERS IN THE INNER ROWS).

view more 

CREDIT: GUOJIN ZHANG, MA LABORATORY, PENN STATE




UNIVERSITY PARK, Pa. — The sunflower family tree revealed that flower symmetry evolved multiple times independently, a process called convergent evolution, among the members of this large plant family, according to a new analysis. The research team, led by a Penn State biologist, resolved more of the finer branches of the family tree, providing insight into how the sunflower family — which includes asters, daisies and food crops like lettuce and artichoke — evolved.

A paper describing the analysis and findings, which researchers said may help identify useful traits to selectively breed plants with more desirable characteristics, appeared online in the journal Plant Communication.

“Convergent evolution describes the independent evolution of what appears to be the same trait in different species, like wings in birds and bats,” said Hong Ma, Huck Chair in Plant Reproductive Development and Evolution, professor of biology in the Eberly College of Science at Penn State and the leader of the research team. “This can make it difficult to determine how closely related two species are by comparing their traits, so having a detailed family tree based on DNA sequence is crucial to understanding how and when these traits evolved.”

The sunflower head, for example, is actually a composite composed of multiple much smaller flowers. While the head is generally radially symmetric — it can be divided into two equal halves in multiple directions like a starfish or a pie — the individual flowers can have different forms of symmetry. According to the new study, bilateral symmetry — where there is only one line that divides the flower into two equal halves — has evolved and been lost multiple times independently in sunflowers over evolutionary history. The researchers found that this convergent evolution is likely related to changes in the number of copies and the expression patterns of the floral regulatory gene, CYC2.

In recent years, many family trees for a group of related species have been built by extensively using transcriptomes, which are the genetic sequences of essentially all of the genes expressed by a species, the researchers explained. Transcriptomes are easier to acquire than high-quality whole-genome sequences for a species but are still difficult and costly to prepare and require fresh plant samples. To increase the number of species available for comparison the team turned to low-coverage genome sequences, which are produced through a process called genome skimming and are relatively inexpensive and easy to prepare, even from dried plant samples.

“To get an accurate whole-genome sequence for a species, each letter of its DNA alphabet must be read — or covered — multiple times to minimize errors,” Ma said. “For the purposes of building a family tree, we show in this paper that we can get away with lower coverage genome sequences. This allowed us to increase the number of species in our analysis, which, in turn, allowed us to resolve more of the finer branches on the sunflower family tree.”

The team used a combination of publicly available and newly generated  transcriptomes, along with a large number of newly obtained skimmed genomes, for a total of 706 species with representatives from 16 subfamilies, 41 tribes and 144 subtribe-level groups in the sunflower family. The subfamilies are major subdivisions of the family, while the tribes and subtribe can contain one or more of genera, which is the classification level just above the species.

“Previous versions of the sunflower family tree had established the relationships among most of the subfamilies and many tribes, which are equivalent to the main branches of a tree,” Ma said. “With our increased sample size, we were able to resolve more of the smaller branches and twigs at the subtribe and genus level. This higher-resolution tree allowed us to reconstruct where and when traits like flower symmetry evolved, demonstrating that bilateral symmetry must have evolved many times independently.”

The team also studied the molecular evolution of genes involved in flower development among sunflowers. They found that one of these genes, CYC2, which is found in multiple copies in the genomes of each species, was activated in species with bilaterally symmetric flowers, suggesting that it might be part of the molecular basis for the convergent evolution of this trait. To further test this, the team performed experiments to quantify CYC2 gene expression in the flowers of species with different types of symmetry.

“Our analysis showed a clear relationship between CYC2 expression and flower symmetry, suggesting that changes in how these genes are used in various sunflower species is likely involved in the convergent evolution observed in the family,” Ma said. “The sunflower family is one of the two largest families of flowering plants containing over 28,000 species, including many economically important agricultural and horticultural species. Understanding how these species are related to one another allows us to determine how and when their traits evolved. This knowledge could also be used to identify useful traits that could be bred into domesticated species from closely related wild ones.”

In addition to Ma, the research team includes Guojin Zhang at Penn State; Junbo Yang, Jie Cai, Zhi-Rong Zhang and Lian-Ming Gao at the Kunming Institute of Botany in Kunming, China; Caifei Zhang at the Wuhan Botanical Garden and Sino-Africa Joint Research Centre in Wuhan, China; Bohan Jiao and Tiangang Gao at the State Key Laboratory of Plant Diversity and Specialty Crops in Beijing, China; and Jose L. Panero at the University of Texas, Austin.

Funding from the Eberly College of Science and the Huck Institutes of the Life Sciences at Penn State, the Strategic Priority Research Program of the Chinese Academy of Sciences, the Large-scale Scientific Facilities of the Chinese Academy of Sciences, and the National Natural Science Foundation of China supported this research.

 

UConn researchers closer to near real-time disaster monitoring


Information that once could take weeks to gather now only takes four days with a new method



UNIVERSITY OF CONNECTICUT





When disaster hits, a quick and coordinated response is needed, and that requires data to assess the nature of the damage, the scale of response needed, and to plan safe evacuations. From the ground, this data collection can take days or weeks, but a team of UConn researchers has found a way to drastically cut the lag time for these assessments using remote sensing data and machine learning, bringing disturbance assessment closer to near real-time (NRT) monitoring. Their findings are published in Remote Sensing of Environment.

Su Ye, a post-doctoral researcher in UConn’s Global Environmental Remote Sensing Laboratory (GERS) and the paper’s first author, says he was inspired by methods used by biomedical researchers to study the earliest symptoms of infections.

“It’s a very intuitive idea,” says Ye. “For example, with COVID, the early symptoms can be very subtle, and you cannot tell it’s COVID until several weeks later when the symptoms become severe and then they confirm infection.”

Ye explains this method is called retrospective chart review (RCR) and it is especially helpful in learning more about infections that have a long latency period between initial exposure to the development of obvious infection.

“This research uses the same ideas. When we’re doing land disturbance monitoring of things like disasters or diseases in forests, for example, at the very beginning of our remote sensing observations, we may have very few or only one remote sensing image, so catching the symptoms early could be very beneficial,” says Ye.

Several days or weeks after a disturbance, researchers can confirm a change, and much like a patient diagnosed with COVID, Ye reasoned they could trace back and do a retrospective analysis to see if earlier signals could be found in the data and if those data could be used to construct a model for near real-time monitoring.

Ye explains that they have a wealth of data to work with – for example, Landsat data stretches back 50 years – so the team could perform a full retrospective analysis to help create an algorithm that can detect changes much faster than current methods which rely on a more manual approach.

“There is so much data and many good products but we have never taken full advantage of them to retrospectively analyze the symptoms for future analysis. We have never connected the past and the future, but this work is bringing these two together.”

Associate Professor in the Department of Natural Resources and the Environment and Director of the GERS Laboratory Zhe Zhu says they used the multitudes of data available and applied machine learning, along with physical barriers to pioneer a technique that pushes the boundary of near real-time detection to, at most, four days as opposed to a month or more.

Until now, early detection was more challenging, because it is harder to differentiate change in the early post-disturbance stages, says Zhu.

“These data contain a lot of noise caused by things like clouds, cloud shadows, smoke, aerosols, even the changing of the seasons, and accounting for these variations makes the interpretation of real change on the Earth’s surface difficult, especially when the goal is to detect those disturbances as soon as possible.”

A key point in developing the method is the open access to the most advanced data available at medium-resolution, says Ye.

“Scientists in the United States are in collaboration with European scientists, and we combine all four satellites, so we have built upon the work of many, many others. Satellite technologies like Landsat – I think that’s one of the greatest projects in human history.”

Beyond making the images open source, Zhu adds that the data set – NASA Harmonized Landsat and Sentinel-2 data (HLS) — was harmonized by a team at NASA, meaning the Landsat and Sentinel-2 data were all calibrated to the same resolution, which saves a lot of processing time and allows researchers to start working with the data directly,

“Without the NASA HLS data, we may spend months to just get the data ready.”

Ye explains they set thresholds based on empirical knowledge from what was seen in previous land disturbances. They look at signals in the data, called spectral change, and calculate the overall magnitude of change to help distinguish the noise from the early signals of disturbances. This approach ignores other relevant important disturbance-related information such as spectral change angle, patterns of seasonality, pre-disturbance land condition, says Ye.

“The new method lets the past data supervise us to find the real signals. For example, some disturbances occur in certain seasons, so similarity could be taken into account, and some disturbances have special spectral features that will increase at certain bands, but decrease in other bands. We can then use the data to build a model to better characterize the changes.”

On the other hand, we took advantage of numerous existing disturbance products that could be used as training data in machine learning and AI, says Zhu.

“Once this massive amount of training data is collected, there can be some wrong pixels, but this machine learning approach can further refine the results and provide better results. It’s as if the physical, statistical rules are talking to the machine learning approach and they work together to improve the results.”

Co-author and Postdoctoral Researcher Ji Won Suh says the team is eager to continue working on this method and to monitor land disturbances nationwide.

“For future directions, I hope we can help to tell the story about socio-economic impacts and what is going on in our earth system. If denser times series data are available, and more data storage is available, together with this algorithm, we can understand our system more intuitively. I’m very much looking forward to the future.”

Zhu says the approach is already attracting interest, and he expects the interest will grow. Their work is open source and Zhu says they are happy to help other groups adopt the method. The platform has already been used for near-real-time disaster monitoring. In the aftermath of Hurricane Ian, the team quickly employed this method to aid in the recovery efforts.

“I think it is extremely beneficial,” says Zhu. “If any kind of disaster happens, we can see the damage in the area quickly and determine the extent and the estimated cost for recovery. We’re hoping to have this comprehensive land disturbance monitoring system in near real-time to help people reduce the damage from those big disasters.”