Identification of an autotransporter peptidase of Rickettsia rickettsii responsible for maturation of surface exposed autotransporters.

Members of the spotted fever group rickettsia express four large, surface-exposed autotransporters, at least one of which is a known virulence determinant. Autotransporter translocation to the bacterial outer surface, also known as type V secretion, involves formation of a ß-barrel autotransporter domain in the periplasm that inserts into the outer membrane to form a pore through which the N-terminal passenger domain is passed and exposed on the outer surface. Two major surface antigens of Rickettsia rickettsii, are known to be surface exposed and the passenger domain cleaved from the autotransporter domain. A highly passaged strain of R. rickettsii, Iowa, fails to cleave these autotransporters and is avirulent. We have identified a putative peptidase, truncated in the Iowa strain, that when reconstituted into Iowa restores appropriate processing of the autotransporters as well as restoring a modest degree of virulence.

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.

MOTIVATION: Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes. RESULTS: Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction. AVAILABILITY AND IMPLEMENTATION: The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.

Enhanced Mineralization of Organic Pollutants through Atomic Hydrogen-Mediated Alternative Transformation Pathways.

Electrocatalytic hydrogen atom-hydroxyl radical (H*-·OH) redox system is a promising approach for contaminant removal and mineralization. However, its working mechanism, especially the effect of H*, remains unclear, hindering its practical application. Herein, we constructed an electrochemical reactor equipped with our self-made Pd-loaded Ti/TiO2 nanotube cathode and a commercial boron-doped diamond anode. After fulfilling the electrode characterization and free radical detection, we employed coumarin and 7-azido-4-methylcoumarin as probes to confirm the participation of H* in the transformation of organic compounds. A comprehensive study on the degradation kinetics, reaction, and mineralization mechanisms using benzoic acid (BA) and 4-chlorophenol (4-CP) as model compounds was further conducted. The rate constants and total organic carbon removal of BA and 4-CP in the redox system increased compared with those of the individual oxidation and reduction processes. Theoretical calculations demonstrate that H* opens up alternative pathways for BA and 4-CP ring cleavage, forming quinones as reactive intermediates. Furthermore, H* facilitates the mineralization of the typical intermediates, maleic acid and fumaric acid, through C=C bond addition and H-abstraction from the 1,1-diol structure. The presence of H* provides alternative pathways for pollutant transformation, consequently reducing the treatment duration.

Assembly of a dsRNA synthesizing complex: RNA-DEPENDENT RNA POLYMERASE 2 contacts the largest subunit of NUCLEAR RNA POLYMERASE IV.

In plants, transcription of selfish genetic elements such as transposons and DNA viruses is suppressed by RNA-directed DNA methylation. This process is guided by 24-nt short-interfering RNAs (siRNAs) whose double-stranded precursors are synthesized by DNA-dependent NUCLEAR RNA POLYMERASE IV (Pol IV) and RNA-DEPENDENT RNA POLYMERASE 2 (RDR2). Pol IV and RDR2 coimmunoprecipitate, and their activities are tightly coupled, yet the basis for their association is unknown. Here, we show that an interval near the RDR2 active site contacts the Pol IV catalytic subunit, NRPD1, the largest of Pol IV's 12 subunits. Contacts between the catalytic regions of the two enzymes suggests that RDR2 is positioned to rapidly engage the free 3' ends of Pol IV transcripts and convert these single-stranded transcripts into double-stranded RNAs (dsRNAs).

Nicotine improves DSS-induced colitis by inhibiting NLRP3 and altering gut microbiota.

Ulcerative colitis (UC) is a chronic recurrent inflammatory disease affecting the rectum and colon. Numerous epidemiological studies have identified smoking as a protective factor for UC. Dysbiosis of intestinal microbiota and release of inflammatory factors are well-established characteristics associated with UC. Therefore, we have observed that nicotine exhibits the potential to ameliorate colitis symptoms in UC mice. Additionally, it exerts a regulatory effect on colonic microbiota dysbiosis by promoting the growth of beneficial bacteria while suppressing harmful bacteria. Combined in vivo and in vitro investigations demonstrate that nicotine primarily impedes the assembly of NLRP3, subsequently inhibiting downstream IL-1ß secretion.

Occurrence, fate, and risk assessment of antibiotics in conventional and advanced drinking water treatment systems: From source to tap.

The occurrence and removal of 38 antibiotics from nine classes in two drinking water treatment plants (WTPs) were monitored monthly over one year to evaluate the efficiency of typical treatment processes, track the source of antibiotics in tap water and assess their potential risks to ecosystem and human health. In both source waters, 18 antibiotics were detected at least once, with average total antibiotic concentrations of 538.5 ng/L in WTP1 and 569.3 ng/L in WTP2. The coagulation/flocculation and sedimentation, sand filtration and granular activated carbon processes demonstrated limited removal efficiencies. Chlorination, on the other hand, effectively eliminated antibiotics by 48.7 ± 11.9%. Interestingly, negative removal was observed along the distribution system, resulting in a significant antibiotic presence in tap water, with average concentrations of 131.5 ng/L in WTP1 and 362.8 ng/L in WTP2. Source tracking analysis indicates that most antibiotics in tap water may originate from distribution system. The presence of antibiotics in raw water and tap water posed risks to the aquatic ecosystem. Untreated or partially treated raw water could pose a medium risk to infants under six months. Water parameters, for example, temperature, total nitrogen and total organic carbon, can serve as indicators to estimate antibiotic occurrence and associated risks. Furthermore, machine learning models were developed that successfully predicted risk levels using water quality parameters. Our study provides valuable insights into the occurrence, removal and risk of antibiotics in urban WTPs, contributing to the broader understanding of antibiotic pollution in water treatment systems.

Production of non-natural 5-methylorsellinate-derived meroterpenoids in Aspergillus oryzae.

Fungal meroterpenoids are diverse structurally intriguing molecules with various biological properties. One large group within this compound class is derived from the aromatic precursor 3,5-dimethylorsellinic acid (DMOA). In this study, we constructed engineered metabolic pathways in the fungus Aspergillus oryzae to expand the molecular diversity of meroterpenoids. We employed the 5-methylorsellinic acid (5-MOA) synthase FncE and three additional biosynthetic enzymes for the formation of (6R,10'R)-epoxyfarnesyl-5-MOA methyl ester, which served as a non-native substrate for four terpene cyclases from DMOA-derived meroterpenoid pathways. As a result, we successfully generated six unnatural 5-MOA-derived meroterpenoid species, demonstrating the effectiveness of our approach in the generation of structural analogues of meroterpenoids.

Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media.

BACKGROUND: COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. OBJECTIVE: This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. METHODS: From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. RESULTS: In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. CONCLUSIONS: The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study.

Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, capable of predicting slag inclusion defects in continuous-casting slabs, based on process condition sensor data. A large dataset consisting of sensor data from nearly 7300 casting samples has been analyzed, with the empirical mode decomposition (EMD) algorithm utilized to process the multi-modal time series. The following machine-learning algorithms have been examined: K-Nearest neighbors, support vector classifier (linear and nonlinear kernels), decision trees, random forests, AdaBoost, and Artificial Neural Networks. Four over-sampling or under-sampling algorithms have been adopted to solve imbalanced data distribution. In the experiment, the optimized random forest outperformed other machine-learning algorithms in terms of recall and ROC AUC, which could provide valuable insights for quality control.

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data.

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.

An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems.

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.

Impact of China's establishment of ecological civilization pilot zones on carbon dioxide emissions.

Since China's speedy economic development, the problems of energy consumption and environmental pollution have become increasingly prominent. A question worth in-depth study is whether ecological civilization (EC) construction, one of the main policies to solve China's severe environmental and ecological problems, affects carbon dioxide emissions (CDE). In this study, we use a novel method proposed by Hsiao et al. (2012) to estimate the average treatment effect by using panel data to study the impact of establishing the EC pilot zones of China on CDE. The results show that, although EC construction is effective in reducing CDE, the impact varies from region to region. The study findings can provide a scientific basis to promote the experience of EC construction better and improve its CDE effect.

Thermal oxidation of degradation products from thermoplastic polyvinyl alcohol: Determination on oxidation temperature and residence time.

The degradable protective articles made of thermoplastic polyvinyl alcohol (TPVA) are widely used in nuclear power plants, and they are thermally decomposed after use to reduce solid waste. However, in the real decomposition of TPVA, the temperature in the oxidation reactor is not self-sustaining; as a result, the degradation products contain a lot of CO, resulting in more pollution and energy waste. In this paper, jet stirred reactor (JSR) and Chemkin software were used to study the reaction kinetics characteristics of the oxidation process of degradation products from TPVA in the range of 550 °C-700 °C. Both experiments and kinetic simulation show that a higher average temperature of the oxidation reactor is needed to achieve lower CO emissions. When using 5% or 10% TPVA degradation solution, the average temperature should not befall below 625 °C or 675 °C. The corresponding residence time should be greater than 6 s and 5 s respectively. The combination of research findings and engineering practice provides great help to the optimization of the actual work process.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens.

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

Effects of dietary berberine on growth performance, lipid metabolism, antioxidant capacity and lipometabolism-related genes expression of AMPK signaling pathway in juvenile black carp (Mylopharyngodon piceus) fed high-fat diets.

This study aimed to investigate the effects of high-fat diet (HFD) supplemented with berberine on growth, lipid metabolism, antioxidant capacity and lipometabolism-related genes expression of AMPK signaling pathway in juvenile black carp (Mylopharyngodon piceus). Five hundred and forty healthy fish (4.04 ± 0.01 g) were randomly distributed into six groups, and fed six experimental diets: normal-fat diet (NFD, 5% fat), HFD (15% fat), and four HFDs supplemented with graded levels of berberine, respectively. The results showed that, compared with fish fed NFD, HFD had no effects on the growth of fish except for reducing survival rate, whereas HFD caused extensive lipid accumulation, oxidative stress injury and hepatic abnormalities. However, compared with the HFD group, fish fed HFD containing an appropriate berberine (98.26 or 196.21 mg/kg) improved the growth performance, increased hepatic lipid metabolism and antioxidant enzymes activities, and up-regulated the mRNA expression levels of ampk subunits and lipolysis genes such as pparα, cpt-1, acox, atgl and hsl (P < 0.05). Meanwhile, HFD supplemented with an appropriate berberine reduced crude lipid contents in liver and whole-body, decreased serum lipid contents, and ALT and AST activities, and down-regulated the mRNA expression levels of lipogenesis genes such as srebp-1, acc1, gpat, fas and pparγ, and lipid transporter genes such as fatp, fabp and fat/cd36 (P < 0.05). Thus, HFD supplemented with an appropriate berberine could improve growth of black carp, promote lipid metabolism and enhance antioxidant capacity. The lipid-lowering mechanism of berberine might be mediated by activating AMPK pathway, up-regulating lipolysis genes expression, and down-regulating lipogenesis and transport genes expression.

Spatial-Temporal Pattern and Influencing Factors of Ecological Efficiency in Zhejiang-Based on Super-SBM Method.

The traditional meaning of ecological efficiency generally considers only the ratio of economic output to environmental input. This paper expands the meaning and the evaluation system of ecological efficiency from the perspective of improving people's livelihoods. Not only are the discharge of wastewater, waste gas, and solid waste included in the undesired output, but the output index also takes full account of the overall development of the economy, innovation, society and the environment from the perspective of high-quality development. Under the assumption of variable returns to scale, a super-efficiency slack-based measure model based on the undesirable output and Malmquist index is introduced to measure the spatial and temporal variation of ecological efficiency of Zhejiang Province in China, and the panel Tobit method is used to study the key factors affecting ecological efficiency. The results include the four following findings: (1) In the past 12 years, the ecological efficiency of Zhejiang Province has steadily increased, except in 2019 and 2020, when seven cities in Zhejiang Province experienced a decline or near stagnation due to the impact of the economic slowdown and the COVID-19 epidemic. (2) The ecological efficiency of Zhejiang demonstrates a severe regional imbalance, showing a high level in the northeast and a low level in the southwest. (3) Malmquist index analysis shows that the improvement of ecological efficiency in Zhejiang Province has shifted from mainly relying on the dual drivers of pure technical efficiency and scale efficiency in the early stage to relying on technological progress in the later stage. (4) Tobit regression analysis shows that industrialization structure, Theil index, and traffic activity have a significant positive effect on ecological efficiency.

Identifying host-specific amino acid signatures for influenza A viruses using an adjusted entropy measure.

BACKGROUND: Influenza A viruses (IAV) exhibit vast genetic mutability and have great zoonotic potential to infect avian and mammalian hosts and are known to be responsible for a number of pandemics. A key computational issue in influenza prevention and control is the identification of molecular signatures with cross-species transmission potential. We propose an adjusted entropy-based host-specific signature identification method that uses a similarity coefficient to incorporate the amino acid substitution information and improve the identification performance. Mutations in the polymerase genes (e.g., PB2) are known to play a major role in avian influenza virus adaptation to mammalian hosts. We thus focus on the analysis of PB2 protein sequences and identify host specific PB2 amino acid signatures. RESULTS: Validation with a set of H5N1 PB2 sequences from 1996 to 2006 results in adjusted entropy having a 40% false negative discovery rate compared to a 60% false negative rate using unadjusted entropy. Simulations across different levels of sequence divergence show a false negative rate of no higher than 10% while unadjusted entropy ranged from 9 to 100%. In addition, under all levels of divergence adjusted entropy never had a false positive rate higher than 9%. Adjusted entropy also identifies important mutations in H1N1pdm PB2 previously identified in the literature that explain changes in divergence between 2008 and 2009 which unadjusted entropy could not identify. CONCLUSIONS: Based on these results, adjusted entropy provides a reliable and widely applicable host signature identification approach useful for IAV monitoring and vaccine development.

Identification of a substrate-like cleavage-resistant thrombin inhibitor from the saliva of the flea Xenopsylla cheopis.

The salivary glands of the flea Xenopsylla cheopis, a vector of the plague bacterium, Yersinia pestis, express proteins and peptides thought to target the hemostatic and inflammatory systems of its mammalian hosts. Past transcriptomic analyses of salivary gland tissue revealed the presence of two similar peptides (XC-42 and XC-43) having no extensive similarities to any other deposited sequences. Here we show that these peptides specifically inhibit coagulation of plasma and the amidolytic activity of α-thrombin. XC-43, the smaller of the two peptides, is a fast, tight-binding inhibitor of thrombin with a dissociation constant of less than 10 pM. XC-42 exhibits similar selectivity as well as kinetic and binding properties. The crystal structure of XC-43 in complex with thrombin shows that despite its substrate-like binding mode, XC-43 is not detectably cleaved by thrombin and that it interacts with the thrombin surface from the enzyme catalytic site through the fibrinogen-binding exosite I. The low rate of hydrolysis was verified in solution experiments with XC-43, which show the substrate to be largely intact after 2 h of incubation with thrombin at 37 °C. The low rate of XC-43 cleavage by thrombin may be attributable to specific changes in the catalytic triad observable in the crystal structure of the complex or to extensive interactions in the prime sites that may stabilize the binding of cleavage products. Based on the increased arterial occlusion time, tail bleeding time, and blood coagulation parameters in rat models of thrombosis XC-43 could be valuable as an anticoagulant.

AvrRpm1 Functions as an ADP-Ribosyl Transferase to Modify NOI Domain-Containing Proteins, Including Arabidopsis and Soybean RPM1-Interacting Protein4.

The Pseudomonas syringae effector protein AvrRpm1 activates the Arabidopsis (Arabidopsis thaliana) intracellular innate immune receptor protein RESISTANCE TO PSEUDOMONAS MACULICOLA1 (RPM1) via modification of a second Arabidopsis protein, RPM1-INTERACTING PROTEIN4 (AtRIN4). Prior work has shown that AvrRpm1 induces phosphorylation of AtRIN4, but homology modeling indicated that AvrRpm1 may be an ADP-ribosyl transferase. Here, we show that AvrRpm1 induces ADP-ribosylation of RIN4 proteins from both Arabidopsis and soybean (Glycine max) within two highly conserved nitrate-induced (NOI) domains. It also ADP ribosylates at least 10 additional Arabidopsis NOI domain-containing proteins. The ADP-ribosylation activity of AvrRpm1 is required for subsequent phosphorylation on Thr-166 of AtRIN4, an event that is necessary and sufficient for RPM1 activation. We also show that the C-terminal NOI domain of AtRIN4 interacts with the exocyst subunits EXO70B1, EXO70E1, EXO70E2, and EXO70F1. Mutation of either EXO70B1 or EXO70E2 inhibited secretion of callose induced by the bacterial flagellin-derived peptide flg22. Substitution of RIN4 Thr-166 with Asp enhanced the association of AtRIN4 with EXO70E2, which we posit inhibits its callose deposition function. Collectively, these data indicate that AvrRpm1 ADP-ribosyl transferase activity contributes to virulence by promoting phosphorylation of RIN4 Thr-166, which inhibits the secretion of defense compounds by promoting the inhibitory association of RIN4 with EXO70 proteins.plantcell;31/11/2664/FX1F1fx1.

AvrRpm1 Functions as an ADP-Ribosyl Transferase to Modify NOI-domain Containing Proteins, Including Arabidopsis and Soybean RPM1-interacting Protein 4.

The Pseudomonas syringae effector protein AvrRpm1 activates the Arabidopsis intracellular innate immune receptor protein RPM1 via modification of a second Arabidopsis protein, RIN4. Prior work has shown that AvrRpm1 induces phosphorylation of AtRIN4, but homology modeling indicated that AvrRpm1 may be an ADP-ribosyl transferase. Here we show that AvrRpm1 induces ADP-ribosylation of RIN4 proteins from both Arabidopsis and soybean within two highly conserved nitrate-induced (NOI) domains. It also ADP-ribosylates at least ten additional Arabidopsis NOI domain-containing proteins. The ADP-ribosylation activity of AvrRpm1 is required for subsequent phosphorylation on threonine 166 of Arabidopsis RIN4, an event that is necessary and sufficient for RPM1 activation. We also show that the C-terminal NOI domain of AtRIN4 interacts with the exocyst subunits EXO70B1, EXO70E1, EXO70E2 and EXO70F1. Mutation of either EXO70B1 or EXO70E2 inhibited secretion of callose induced by the bacterial flagellin-derived peptide flg22. Substitution of RIN4 threonine 166 with aspartate enhanced the association of AtRIN4 with EXO70E2, which we posit inhibits its callose deposition function. Collectively, these data indicate that AvrRpm1 ADP-ribosyl transferase activity contributes to virulence by promoting phosphorylation of RIN4 threonine 166, which inhibits the secretion of defense compounds by promoting the inhibitory association of RIN4 with EXO70 proteins.