Tuesday, January 06, 2026

New study overturns long-held model of how plants coordinate immune responses.


Rapid local and systemic jasmonate signalling drives initiation and establishment of plant systemic immunity




University of Warwick

Jasmonate activity is included systemically by pathogens. 

video: 

Jasmonate activity is included systemically by pathogens. Here plants are infected by bacteria carrying different avirulence genes (avrRpm1 at bottom right, avrRps4 at bottom left, avrRpt2 at top right). A systemic immune response triggered by each Avr gene starts at different times as measured by jasmonate signalling (via JISS1:LUC). The JISS1:LUC activity lights up starting at the infiltrated leaf (*) and then spreading through the plant to adjacent and distant leaves, the earliest of which happens in under 4 hours (Bottom right). A bacteria without an avirulence gene (top left) acts as a control as the plant shows no immune response across the 24 hour period.

view more 

Credit: Gaikwad, T., Breen, S., Breeze, E., Stroud, E. et al. Nature Plants (2026). https://doi.org/10.1038/s41477-025-02178-4





University of Warwick researchers discover rapid, jasmonate-driven, early immune response in plants using breakthrough live-imaging tool.

Plants mobilise their immune defences far earlier than scientists have believed for decades — and through a previously overlooked early signalling mechanism - according to a new study published in Nature Plants.

Unlike animals, plants are literally rooted to the spot and cannot deploy specialised immune cells or antibodies, nor run away. Instead, every cell must be capable of responding to attack from pathogenic viruses, bacteria, fungi, or insect pests. When attacked plants quickly initiate defence responses at the site of challenge, but they can also activate immune responses in distant, not yet infected tissues to protect the rest of the plant, a process known as Systemic Acquired Resistance (SAR).

 For decades, SAR has been understood to rely on the signalling molecule salicylic acid — supported by N-hydroxypipecolic acid — to execute and maintain long-lasting immune protection throughout the plant. These molecules are synthesised following infection and gradually accumulate in distant uninfected tissues.

The Warwick team now shows that before this salicylic acid-centred defence is established, plants deploy a much faster communication system: a wave of jasmonate-dependent immune signals that spreads through the plant within just a few hours, initiating SAR well before classical measures of activated SAR.

“What we show here is that whole-plant immunity is activated much faster than we ever realised,” said Professor Murray Grant, Elizabeth Creak Chair in Food Security at the University of Warwick and senior author of the study. “Classic salicylic acid–based SAR is still vital, but our work reveals a new early-warning system powered by jasmonates — hormones previously thought to suppress salicylic acid based immune response.”

“Whereas salicylic acid accumulation can take more than 24 hours, the jasmonate-dependent signal appeared within three to four hours of infection, moving rapidly through the plant’s epidermal and vascular tissues to the uninfected leaves. It is a fundamental shift in our understanding of how plant immunity works.”

Watching immunity spread in real time

To uncover this hidden early SAR phase, the researchers developed a novel jasmonate-linked SAR reporter, JISS1:LUC, which functions as a molecular tracker for this early immune activation. This tool allowed them to visualise immune signals moving out of infected leaves and across into uninfected leaves in real time.

This very early signalling phase has remained hidden until now because most traditional approaches detect immune responses during or after systemic defences are fully established, measuring classical molecular markers or SA itself, well after these jasmonate-driven signals are developed.

The results point to a multi-phase SAR strategy. “Jasmonates sound the alarm,” explained Dr Erin Stroud, Research Fellow in the School of Life Sciences at Warwick and joint first author. “They coordinate a fast, mobile immune signal, alerting the entire plant that trouble is coming. Classic signalling compounds such as salicylic acid and N-hydroxypipecolic acid then strengthen and stabilises these defences to ensure long-lasting protection.”

This study showed that even in plants unable to produce or perceive salicylic acid, the early wave of signalling occurred — but SAR disappeared when jasmonate biosynthesis was disrupted. Those plants lacking jasmonate signalling mounted normal local immune responses to infection, but failed to protect distant leaves, making them vulnerable to secondary infections.

New possibilities for crop protection

Unexpectedly, the team also found that the jasmonate signalling is required to underpin plant-wide electrical signalling, similar to signals previously linked to wound and herbivore responses.

“These electrical signals are similar to those elicited by herbivory and require functional jasmonate  signalling to allow this rapid long-distance communication,” said Dr Emily Breeze, Assistant Professor at Warwick and joint first author. “Our JISS1:LUC reporter system is an excellent tool for visualising early jasmonate-based SAR initiation in real time, within hours of local attack, which gives us a unique method to explore how plants integrate hormones, calcium fluxes and bioelectricity signals to ultimately protect themselves against invaders.”

The discovery that both jasmonate and electrical signalling are elaborated during early systemic immunity opens new possibilities for engineering crops that respond to infection more quickly, limiting disease spread and yield loss, particularly under conditions where pathogens spread quickly or plants face multiple pathogen threats simultaneously.

Professor Grant concluded: “This work not only reshapes our understanding of systemic plant immunity but understanding common SAR signalling mechanisms gives us a unique lead to design strategies for bioengineering defence systems that provide broad spectrum, rather than pathogen specific crop resistance.

“Specifically, activation of systemic immunity via conditional activation of early jasmonate signalling could provide a novel approach to mitigate crop losses to devastating diseases such as rusts, blights and mildews without the needs for environmentally damaging chemical control.”

ENDS

The paper ‘Rapid local and systemic jasmonate signaling drives initiation and establishment of plant systemic immunity’ is published in Nature Plants. DOI:10.1038/s41477-025-02178-4

This work was funded by multiple BBSRC/UKRI grants (BB/P002560/1, BB/X013049/1, BB/W007126/1, BB/S506783/1), the Leverhulme Trust (RPG-2013-275) and the National Science Foundation (MCB-2435880).

Notes to Editors

For more information please contact:

Matt Higgs, PhD | Media & Communications Officer (Warwick Press Office)

Email: Matt.Higgs@warwick.ac.uk | Phone: +44(0)7880 175403

About the University of Warwick

Founded in 1965, the University of Warwick is a world-leading institution known for its commitment to era-defining innovation across research and education. A connected ecosystem of staff, students and alumni, the University fosters transformative learning, interdisciplinary collaboration, and bold industry partnerships across state-of-the-art facilities in the UK and global satellite hubs. Here, spirited thinkers push boundaries, experiment, and challenge conventions to create a better world.

Editor’s Box: How plants coordinate immune defence

Unlike animals, plants are sessile and do not have mobile immune cells or antibodies. Instead, each cell must be able to defend itself, and plants rely on internal signalling to coordinate responses across their tissues.

When a pathogen attacks, plants first activate local immunity, which can include the hypersensitive response — a form of controlled cell death that blocks pathogen spread. If this local defence is successful, plants then trigger systemic acquired resistance (SAR), a whole-plant immune state that makes uninfected leaves more resistant to future attacks.

SAR has been known for more than one hundred years, but how it is initiated has remained unclear. The prevailing models have focused on the hormone salicylic acid, believed to accumulate at the infection site and move slowly through the plant, taking around 8–24 hours to prime distant leaves.

The study reported here reveals a much earlier and faster phase of immune signalling. Using a novel live imaging reporter system, researchers observed immune activation spreading to distant tissues within just three to four hours of infection. This early response did not depend on salicylic acid, but instead required jasmonate hormones, calcium signalling, and unexpectedly, initiated long-distance electrical activity across the plant.

The findings suggest that plants employ a two-stage immune strategy: a rapid jasmonate-driven alert system that initiates systemic defence, followed by slower salicylic acid–dependent signalling that stabilises and sustains immunity. This fast-signalling layer helps explain how plants coordinate effective whole-body defence despite lacking a circulating immune system.

 

  

 JISS1 (Jasmonate) expression is induced systemically by infection. White asterisk indicates infiltrated leaf. Images are false coloured by signal intensity, as indicated by individual calibration bars. (A) Luciferase activity in JISS1:LUC plants following DCavrRpm1, DC, DChrpA or mock (MgCl2) challenges at 4:30 hpi. (B) Temporal spatial dynamics of luciferase activity in JISS1:LUC plants following DCavrRpm1 challenge, initiating at 3 hpi. 3.20 hpi, 3.50 hpi and 4.30 hpi images capture the systemic spread of the signal over time. (C) Different Avr genes display temporal specificity in activation of systemic JISS1:LUC; DCavrRpm1 (4 hpi), DCavrRps4 (13:20 hpi) and DCavrRpt2 (15:20 hpi), compared to DChrpA control.

Credit

Gaikwad, T., Breen, S., Breeze, E., Stroud, E. et al. Nature Plants (2026). https://doi.org/10.1038/s41477-025-02178-4


New AI model predicts disease risk while you sleep



AI predicts disease from sleep



Stanford Medicine





A poor night’s sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road. A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological recordings from one night’s sleep to predict a person’s risk of developing more than 100 health conditions.

Known as SleepFM, the model was trained on nearly 600,000 hours of sleep data collected from 65,000 participants. The sleep data comes from polysomnography, a comprehensive sleep assessment that uses various sensors to record brain activity, heart activity, respiratory signals, leg movements, eye movements and more.

Polysomnography is the gold standard in sleep studies that monitor patients overnight in a lab. It is also, the researchers realized, an untapped gold mine of physiological data.

“We record an amazing number of signals when we study sleep,” said Emmanual Mignot, MD, PhD, the Craig Reynolds Professor in Sleep Medicine and co-senior author of the new study, which will publish Jan. 6 in Nature Medicine. “It’s a kind of general physiology that we study for eight hours in a subject who’s completely captive. It’s very data rich.”

Only a fraction of that data is used in current sleep research and sleep medicine. With advances in artificial intelligence, it’s now possible to make sense of much more of it. The new study is the first to use AI to analyze such large-scale sleep data. 

“From an AI perspective, sleep is relatively understudied. There’s a lot of other AI work that’s looking at pathology or cardiology, but relatively little looking at sleep, despite sleep being such an important part of life,” said James Zou, PhD, associate professor of biomedical data science and co-senior author of the study.

Learning the language of sleep

To take advantage of the sleep data trove, the researchers built a foundation model, a type of AI model that can train itself on vast amounts of data and apply what it has learned to a wide range of tasks. Large language models like ChatGPT are examples of foundation models trained on huge amounts of text.

The 585,000 hours of polysomnography data that SleepFM was trained on came from patients who’d had their sleep assessed at various sleep clinics. The sleep data is split into five-second increments, analogous to words that large language models use to train on.

“SleepFM is essentially learning the language of sleep,” Zou said.

The model was able to incorporate multiple streams of data — electroencephalography, electrocardiography, electromyography, pulse reading and breathing airflow, for example — and glean how they relate to each other.

To achieve this, the researchers developed a new training technique, called leave-one-out contrastive learning, that essentially hides one modality of data and challenges the model to reconstruct the missing piece based on the other signals.

“One of the technical advances that we made in this work is to figure out how to harmonize all these different data modalities so they can come together to learn the same language,” Zou said.

Forecasting disease

After the training phase, the researchers could fine-tune the model to different tasks.

First, they tested the model on standard sleep analysis tasks, such as classifying different stages of sleep and diagnosing the severity of sleep apnea. SleepFM performed as well as or better than state-of-the-art models used today.

Then the researchers tackled a more ambitious goal: predicting future disease onset from sleep data. To identify which conditions could be forecast, they needed to pair the training polysomnography data with the long-term health outcomes of the same participants. Fortunately, they had access to more than half a century’s worth of health records from a sleep clinic.

The Stanford Sleep Medicine Center was founded in 1970 by the late William Dement, MD, PhD, widely considered the father of sleep medicine. The largest cohort of patients used to train SleepFM — some 35,000 patients ranging in age from 2 to 96 — had their polysomnography data recorded at the clinic between 1999 and 2024. The researchers paired these patients’ polysomnography data with their electronic health records, which provided up to 25 years of follow-up for some patients.

(The clinic’s polysomnography recordings go back even further, but only on paper, said Mignot, who directed the sleep center from 2010 to 2019.)

SleepFM analyzed more than 1,000 disease categories in the health records and found 130 that could be predicted with reasonable accuracy by a patient’s sleep data. The model’s predictions were particularly strong for cancers, pregnancy complications, circulatory conditions and mental disorders, achieving a C-index higher than 0.8.

The C-index, or concordance index, is a common measure of a model’s predictive performance, specifically, its ability to predict which of any two individuals in a group will experience an event first.

“For all possible pairs of individuals, the model gives a ranking of who’s more likely to experience an event — a heart attack, for instance — earlier. A C-index of 0.8 means that 80% of the time, the model’s prediction is concordant with what actually happened,” Zou said.

SleepFM excelled at predicting Parkinson’s disease (C-index 0.89), dementia (0.85), hypertensive heart disease (0.84), heart attack (0.81), prostate cancer (0.89), breast cancer (0.87) and death (0.84).

“We were pleasantly surprised that for a pretty diverse set of conditions, the model is able to make informative predictions,” Zou said.

Models of less accuracy, with C-indices around 0.7, such as those that predict a patient’s response to different cancer treatments, have proven useful in clinical settings, he added.

Interpreting the model

The team is working on ways to further improve SleepFM’s predictions, perhaps by adding data from wearables, and to understand exactly what the model is interpreting.

“It doesn’t explain that to us in English,” Zou said. “But we have developed different interpretation techniques to figure out what the model is looking at when it’s making a specific disease prediction.”

The researchers note that even though heart signals factor more prominently in heart disease predictions and brain signals factor more prominently in mental health predictions, it was the combination of all the data modalities that achieved the most accurate predictions.

“The most information we got for predicting disease was by contrasting the different channels,” Mignot said. Body constituents that were out of sync — a brain that looks asleep but a heart that looks awake, for example — seemed to spell trouble.

Rahul Thapa, a PhD student in biomedical data science, and Magnus Ruud Kjaer, a PhD student at Technical University of Denmark, are co-lead authors of the study.

Researchers from the Technical University of Denmark, Copenhagen University Hospital –Rigshospitalet, BioSerenity, University of Copenhagen and Harvard Medical School contributed to the work.

The study received funding from the National Institutes of Health (grant R01HL161253), Knight-Hennessy Scholars and Chan-Zuckerberg Biohub.

# # #

 

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu.

New study reveals how students strategically use GenAI for critical reading revision

Research identifies key dimensions students focus on and the factors driving their selective engagement




ECNU Review of Education






GenAI tools are increasingly used in academic settings, yet little is known about how they affect higher-order thinking during critical reading and writing revision. A new study has found that postgraduate students selectively engage with GenAI when revising critical reading reports, focusing intensely on specific analytical dimensions. This strategic engagement is shaped by academic goals, supervisor demands, career aspirations, and misunderstandings of content.

Critical reading and writing are essential skills for academic success. Revising critical reading reports is crucial for developing these competencies, yet students often struggle. With the rise of GenAI tools, students now have new ways to engage with this complex, higher-order thinking process.

In a study published online on October 31, 2025, in ECNU Review of Education, a team of researchers from Macao Polytechnic University and Jiangxi Normal University investigated how 22 postgraduate students used GenAI tools to revise their critical reading reports. Using lag sequential analysis and thematic analysis, the team examined student engagement across ten dimensions of critical reading and writing based on Wallace and Wray’s framework.

“Contrary to assumptions that artificial intelligence might uniformly guide learning, we see students making strategic choices, explained Lin et al.

The study found that students spent significantly more time revising four dimensions: research aims and investigation, research contributions, quality of evidence, and adaptation of theoretical frameworks. This selective engagement was primarily influenced by students’ reading purposes, external demands from supervisors, their career plans as future teachers, and literal misunderstandings of the content.

The researchers found that engagement was lower in dimensions like moral or value preferences, generalization of findings, and consistency with personal experience, often due to a lack of external requirement or perceived personal relevance. “Our findings highlight that GenAI tools are assistants, not replacements, in developing critical thinking. The role of teacher guidance and student agency is important,” concluded Lin et al.