A complex NLR signalling network mediates immunity to diverse plant pathogens

Both plants and animals rely on nucleotide-­binding domain leucine-­rich repeat-­containing (NLR) proteins to respond to invading pathogens and activate immune responses. An emerging concept in NLR biology is that “sensor” NLR proteins are often paired with “helper” NLR proteins to mediate immune signalling. However, the degree to which NLRs form signalling networks beyond sensor and helper pairs is poorly understood. In this thesis, I discovered that a large NLR immune signalling network with a complex architecture mediates immunity to oomycetes, bacteria, viruses, nematodes, and insects. Helper NLRs in the NRC (NLR-­required for cell death) family are functionally redundant but display distinct specificities towards diverse sensor NLRs. Several sensor NLRs, including Rx, Bs2 and Sw5b, signal via interchangeable NRC2, NRC3 or NRC4, whereas some other sensor NLRs have a more limited downstream spectrum. For example, Prf signals via interchangeable NRC2 or NRC3 but not NRC4, and Rpi-­blb2 signals via only NRC4. These helper/sensor NLRs form a unique phylogenetic superclade, with the NRC clade sister to the sensor NLR clades. The network has emerged over 100 million years ago from an NLR pair that diversified into up to one half of the NLRs of asterids. I propose that this NLR network increases evolvability and robustness of immune signalling to counteract rapidly evolving plant pathogens

Spatial competitive games with disingenuously delayed positions

Citation: Soltanolkottabi, M., Ben-Arieh, D., & Wu, C.H. (2017). Spatial competitive games with disingenuously delayed positions. Manuscript, Kansas State University, Manhattan, KS.During the last decade, spatial games have received great attention from researchers showing the behavior of populations of players over time in a spatial structure. One of the main factors which can greatly affect the destiny of such populations is the updating scheme used to apprise new strategies of players. Synchronous updating is the most common updating strategy in which all players update their strategy at the same time. In order to be able to describe the behavior of populations more realistically several asynchronous updating schemes have been proposed. Asynchronous game does not use a universal and players can update their strategy at different time steps during the play. In this paper, we introduce a new type of asynchronous strategy updating in which some of the players hide their updated strategy from their neighbors for several time steps. It is shown that this behavior can change the behavior of populations but does not necessarily lead to a higher payoff for the dishonest players. The paper also shows that with dishonest players, the average payoff of players is less than what they think they get, while they are not aware of their neighbors’ true strategy

The coming of age of EvoMPMI:evolutionary molecular plant-microbe interactions across multiple timescales

Plant-microbe interactions are great model systems to study co-evolutionary dynamics across multiple timescales. However, mechanistic research on plant-microbe interactions has often been conducted with little consideration of evolutionary concepts and methods. Conversely, evolutionary research has rarely integrated the range of mechanisms and models from the molecular plant-microbe interactions field. In recent years, the incipient field of evolutionary molecular plant-microbe interactions (EvoMPMI) has emerged to bridge this gap. Here, we report on some of the recent advances in EvoMPMI. In particular, we highlight new systems to study microbe interactions with early diverging land plants, and new findings from studies of adaptive evolution in pathogens and plants. By linking mechanistic and evolutionary research, EvoMPMI promises to expand our understanding of plant-microbe interactions

Mathematical Model of Innate and Adaptive Immunity of Sepsis: A Modeling and Simulation Study of Infectious Disease

Citation: Shi, Z. Z., Wu, C. H. J., Ben-Arieh, D., & Simpson, S. Q. (2015). Mathematical Model of Innate and Adaptive Immunity of Sepsis: A Modeling and Simulation Study of Infectious Disease. Biomed Research International, 31. doi:10.1155/2015/504259Sepsis is a systemic inflammatory response (SIR) to infection. In this work, a system dynamics mathematical model (SDMM) is examined to describe the basic components of SIR and sepsis progression. Both innate and adaptive immunities are included, and simulated results in silico have shown that adaptive immunity has significant impacts on the outcomes of sepsis progression. Further investigation has found that the intervention timing, intensity of anti- inflammatory cytokines, and initial pathogen load are highly predictive of outcomes of a sepsis episode. Sensitivity and stability analysis were carried out using bifurcation analysis to explore system stability with various initial and boundary conditions. The stability analysis suggested that the system could diverge at an unstable equilibrium after perturbations if r(t2max) (maximum release rate of Tumor Necrosis Factor- (TNF-) alpha by neutrophil) falls below a certain level. This finding conforms to clinical findings and existing literature regarding the lack of efficacy of anti- TNF antibody therapy

Rapid evolution in plant-microbe interactions - a molecular genomics perspective

Rapid (co-)evolution at multiple timescales is a hallmark of plant-microbe interactions. The mechanistic basis for the rapid evolution largely rests on the features of the genomes of the interacting partners involved. Here, we review recent insights into genomic characteristics and mechanisms that enable rapid evolution of both plants and phytopathogens. These comprise fresh insights in allelic series of matching pairs of resistance and avirulence genes, the generation of novel pathogen effectors, the recently recognised small RNA warfare, and genomic aspects of secondary metabolite biosynthesis. In addition, we discuss the putative contributions of permissive host environments, transcriptional plasticity and the role of ploidy on the interactions. We conclude that the means underlying the rapid evolution of plant-microbe interactions are multifaceted and depend on the particular nature of each interaction

Pathogen manipulation of chloroplast function triggers a light-dependent immune recognition

In plants and animals, nucleotide-binding leucine-rich repeat (NLR) proteins are intracellular immune sensors that recognize and eliminate a wide range of invading pathogens. NLR-mediated immunity is known to be modulated by environmental factors. However, how pathogen recognition by NLRs is influenced by environmental factors such as light remains unclear. Here, we show that the agronomically important NLR Rpi-vnt1.1 requires light to confer disease resistance against races of the Irish potato famine pathogen Phytophthora infestans that secrete the effector protein AVRvnt1. The activation of Rpi-vnt1.1 requires a nuclear-encoded chloroplast protein, glycerate 3-kinase (GLYK), implicated in energy production. The pathogen effector AVRvnt1 binds the full-length chloroplast-targeted GLYK isoform leading to activation of Rpi-vnt1.1. In the dark, Rpi-vnt1.1-mediated resistance is compromised because plants produce a shorter GLYK-lacking the intact chloroplast transit peptide-that is not bound by AVRvnt1. The transition between full-length and shorter plant GLYK transcripts is controlled by a light-dependent alternative promoter selection mechanism. In plants that lack Rpi-vnt1.1, the presence of AVRvnt1 reduces GLYK accumulation in chloroplasts counteracting GLYK contribution to basal immunity. Our findings revealed that pathogen manipulation of chloroplast functions has resulted in a light-dependent immune response

An N-terminal motif in NLR immune receptors is functionally conserved across distantly related plant species

The molecular codes underpinning the functions of plant NLR immune receptors are poorly understood. We used in vitro Mu transposition to generate a random truncation library and identify the minimal functional region of NLRs. We applied this method to NRC4-a helper NLR that functions with multiple sensor NLRs within a Solanaceae receptor network. This revealed that the NRC4 N-terminal 29 amino acids are sufficient to induce hypersensitive cell death. This region is defined by the consensus MADAxVSFxVxKLxxLLxxEx (MADA motif) that is conserved at the N-termini of NRC family proteins and ~20% of coiled-coil (CC)-type plant NLRs. The MADA motif matches the N-terminal a1 helix of Arabidopsis NLR protein ZAR1, which undergoes a conformational switch during resistosome activation. Immunoassays revealed that the MADA motif is functionally conserved across NLRs from distantly related plant species. NRC-dependent sensor NLRs lack MADA sequences indicating that this motif has degenerated in sensor NLRs over evolutionary time

NLR network mediates immunity to diverse plant pathogens

Both plants and animals rely on nucleotide-binding domain and leucine-rich repeat-containing (NLR) proteins to respond to invading pathogens and activate immune responses. An emerging concept of NLR function is that “sensor” NLR proteins are paired with “helper” NLRs to mediate immune signaling. However, our fundamental knowledge of sensor/helper NLRs in plants remains limited. In this study, we discovered a complex NLR immune network in which helper NLRs in the NRC (NLR required for cell death) family are functionally redundant but display distinct specificities toward different sensor NLRs that confer immunity to oomycetes, bacteria, viruses, nematodes, and insects. The helper NLR NRC4 is required for the function of several sensor NLRs, including Rpi-blb2, Mi-1.2, and R1, whereas NRC2 and NRC3 are required for the function of the sensor NLR Prf. Interestingly, NRC2, NRC3, and NRC4 redundantly contribute to the immunity mediated by other sensor NLRs, including Rx, Bs2, R8, and Sw5. NRC family and NRC-dependent NLRs are phylogenetically related and cluster into a well-supported superclade. Using extensive phylogenetic analysis, we discovered that the NRC superclade probably emerged over 100 Mya from an NLR pair that diversified to constitute up to one-half of the NLRs of asterids. These findings reveal a complex genetic network of NLRs and point to a link between evolutionary history and the mechanism of immune signaling. We propose that this NLR network increases the robustness of immune signaling to counteract rapidly evolving plant pathogens

Automatic Diagnosis of Late-Life Depression by 3D Convolutional Neural Networks and Cross-Sample Entropy Analysis From Resting-State fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy \u3e 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD

Predicting Suicidality in Late-Life Depression by 3D Convolutional Neural Network and Cross-Sample Entropy Analysis of Resting-State fMRI

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality