Biology subjects

Cai, W.-J.

Publications and source records attributed to Cai, W.-J..

4 recordsLinked to original sources

bioRxiv2024

High temperature treatment optimized for symbiont suppression in an obligatory gut bacterial symbiosis in the stinkbug Plautia stali

In the era of global warming, much attention has been paid to the possibility that high temperature may influence diverse insects not only directly but also indirectly via effects on their symbiotic microorganisms. The stinkbug Plautia stali develops a midgut symbiotic organ that harbors a specific bacterial symbiont indispensable for its growth and survival. Being maintainable in laboratory and tractable experimentally, P. stali is recently highlighted as a model system to investigate the mechanisms underpinning insect-microbe symbiotic interactions. In this study, we reared newly-emerged adult insects of P. stali under different temperature conditions for 8 days and monitored how their symbiotic organs and symbiotic bacteria are affected. While all insects survived at temperatures from 25{degrees}C to 37{degrees}C, some insects died at 38{degrees}C, 39{degrees}C and 40{degrees}C, wherein mortality rates increased as temperature elevated. While the symbiotic organs of the normal insects exhibited vivid yellow color, the symbiotic organs of the insects reared at 35{degrees}C or higher frequently exhibited abnormal colors such as pale yellow, yellowish white, or white, the extent of which became more severe as temperature elevated. Symbiont quantification revealed that, while the symbiont titers were almost constant for 8 days at 25{degrees}C and 30{degrees}C, the symbiont titers on the 8th day drastically declined to 1/100 at 35 and 1/10000 at 37{degrees}C and 39{degrees}C. Based on these results, we propose that rearing at 37{degrees}C for a week is a recommended treatment regime by which the symbiont is effectively suppressed with minimal damage to the host insect.

physiology↗

bioRxiv2024

Wounding perception by jasmonate-mediated JAZ condensation in plants

Wounding is a common stress experienced by plants, resulting in a quick elicitation of jasmonates (JAs). The widely accepted mechanism of COI1-JAZ coreceptor posits that COI1 directly interacts with the active ligand of JAs, whereas JAZs facilitate to trap the ligand in the binding pocket. Here we show that the multiple JAZs of Arabidopsis undergo phase separation in response to wounding. Taking JAZ1 as an example, we found it is an independent sensor of JA-Ile, the active ligand in most plants. Under basal condition, dispersed JAZ1 interacts with MYC2 to inhibit downstream gene expressions. Upon wounding, JAZ1 binds to JA-Ile and forms condensates, reducing its affinity with MYC2 and tending to recruit COI1 as well as other proteasome related components for quick degradation, thereby accelerating signaling output. Our investigation unveils the underlying mechanism by which JAZ1 regulates swift signaling transduction via phase separation upon the direct perception of JA-Ile.

plant biology↗

bioRxiv2023

Plasma membrane association and resistosome formation of plant helper immune receptors

Intracellular plant immune receptors, termed NLRs, respond to pathogen effectors delivered into plant cells. Activation of NLRs typically confers immunity. Sensor NLRs, involved in effector recognition, are either TIR-NLRs (TNLs) or CC-NLRs (CNLs). Helper NLRs, required for sensor NLR signaling, include CCR-NLRs (RNLs) and a special class of CNLs known as NRCs. Activated TNLs produce small molecules that induce an association between the EDS1/SAG101 heterodimer and the NRG1s helper RNLs. Auto active NRG1s oligomerize and form calcium signaling channels largely localized at the plasma membrane (PM). The molecular mechanisms of helper NLR PM association and effector induced NRG1 oligomerization are not well characterized. We find that both RNLs and NRCs require positively charged residues in the second and fourth helices of their CCR or CC domain for phospholipid binding and PM association before and after activation, despite conformational changes that accompany activation. We demonstrate that effector activation of TNLs induces NRG1 oligomerization at the PM and that the cytoplasmic pool of EDS1/SAG101 is critical for cell death function. EDS1/SAG101 cannot be detected in the oligomerized NRG1 resistosome, suggesting that additional unknown triggers might be required to induce the dissociation of EDS1/SAG101 from the previously described NRG1/EDS1/SAG101 heterotrimer before subsequent NRG1 oligomerization, or that the conformational changes resulting from NRG1 oligomerization abrogate the interface for EDS1/SAG101 association. Our data provide new observations regarding dynamic PM association during helper NLR activation and underpin an updated model for effector induced NRG1 resistosome formation.

plant biology↗

bioRxiv2020

A transferrable and interpretable multiple instance learning model for microsatellite instability prediction based on histopathology images

BackgroundMicrosatellite instability (MSI) is a negative prognostic factor for colorectal cancer (CRC) and can be used as a predictor of success for immunotherapy in pan-cancer. However, current MSI identification methods are not available for all patients. We propose an ensemble multiple instance learning (MIL)-based deep learning model to predict MSI status directly from histopathology images. DesignTwo cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from a self-collected Asian data set (Asian-CRC). The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model are associated with genotypes for model interpretation. ResultsA model called Ensembled Patch Likelihood Aggregation (EPLA) was developed in the TCGA-COAD training set based on two consecutive stages: patch-level prediction and WSI-level prediction. The EPLA model achieved an area-under-the -curve (AUC) of 0.8848 in the TCGA-COAD test set, which outperformed the state-of-the-art approach, and an AUC of 0.8504 in the Asian-CRC after transfer learning. Furthermore, the five pathological imaging signatures identified using the model are associated with genomic and transcriptomic profiles, which makes the MIL model interpretable. Results show that our model recognizes pathological signatures related to mutation burden, DNA repair pathways, and immunity. ConclusionOur MIL-based deep learning model can effectively predict MSI from histopathology images and are transferable to a new patient cohort. The interpretability of our model by association with genomic and transcriptomic biomarkers lays the foundation for prospective clinical research.

bioengineering↗