Exocyst subunit Exo70B2 is linked to immune signaling and autophagy.

During the immune response, activation of the secretory pathway is key to mounting an effective response, while gauging its output is important to maintain cellular homeostasis. The Exo70 subunit of the exocyst functions as a spatiotemporal regulator by mediating numerous interactions with proteins and lipids. However, a molecular understanding of the exocyst regulation remains challenging. We show that, in Arabidopsis thaliana, Exo70B2 behaves as a bona fide exocyst subunit. Conversely, treatment with the salicylic acid (SA) defence hormone analog benzothiadiazole (BTH), or the immunogenic peptide flg22, induced Exo70B2 transport into the vacuole. We reveal that Exo70B2 interacts with AUTOPHAGY-RELATED PROTEIN 8 (ATG8) via two ATG8-interacting motives (AIMs) and its transport into the vacuole is dependent on autophagy. In line with its role in immunity, we discovered that Exo70B2 interacted with and was phosphorylated by the kinase MPK3. Mimicking phosphorylation had a dual impact on Exo70B2: first, by inhibiting localization at sites of active secretion, and second, it increased the interaction with ATG8. Phosphonull variants displayed higher effector-triggered immunity (ETI) and were hypersensitive to BTH, which induce secretion and autophagy. Our results suggest a molecular mechanism by which phosphorylation diverts Exo70B2 from the secretory into the autophagy pathway for its degradation, to dampen secretory activity.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.

Detection of SARS-CoV-2 in Fecal Samples From Patients With Asymptomatic and Mild COVID-19 in Korea.

BACKGROUND & AIMS: Although coronavirus disease 2019 (COVID-19) is characterized by fever and respiratory symptoms, some patients have no or mild symptoms. Severe acute respiratory syndrome-coronavirus (SARS-CoV-2) has been detected in feces of patients. We investigated gastrointestinal symptoms and shedding of virus into feces of patients with asymptomatic or mild COVID-19. METHODS: We collected data from 46 patients (median age, 26 y; 46% men) with asymptomatic or mild COVID-19 (without fever and pneumonia) and prolonged respiratory shedding of SARS-CoV-2, quarantined from April 4, 2020, through April 24, 2020, in Korea. Respiratory specimens included upper respiratory specimens (nasopharyngeal and oropharyngeal swabs) and lower respiratory specimens (sputum), and were collected twice per week. The median interval between COVID-19 diagnosis to the start of fecal sample collection was 37 days (range, 29-41 d); 213 stool specimens were collected from 46 patients. We used real-time reverse-transcription polymerase chain reaction to detect SARS-CoV-2 in the respiratory and fecal specimens. RESULTS: Gastrointestinal manifestations were observed in 16 of the 46 patients (35%); diarrhea was the most common (15%), followed by abdominal pain (11%), dyspepsia (11%), and nausea (2%). Virus RNA was detected in feces from 2 patients without gastrointestinal symptoms (4%). Mean cycle threshold values from the time of quarantine to the time of fecal collection tended to be lower in patients with virus detected in fecal samples than in patients without virus in fecal samples (29.91 vs 33.67 in the first week, 29.47 vs 35.71 in the fifth week, respectively). Shedding of virus into feces persisted until day 50 after diagnosis; fecal samples began to test negative before or at approximately the time that respiratory specimens also began to test negative. CONCLUSIONS: In an analysis of fecal and respiratory specimens from patients with COVID-19 in quarantine in Korea, we found that the gastrointestinal tract could be a route of transmission of SARS-CoV-2 even in patients with asymptomatic or mild disease, with no gastrointestinal symptoms. The viral load of the respiratory specimens appears be related to shedding of the virus into feces in this group of patients.

Regulation of oncogene expression in T-DNA-transformed host plant cells.

Virulent Agrobacterium tumefaciens strains integrate their T-DNA into the plant genome where the encoded agrobacterial oncogenes are expressed and cause crown gall disease. Essential for crown gall development are IaaH (indole-3-acetamide hydrolase), IaaM (tryptophan monooxygenase) and Ipt (isopentenyl transferase), which encode enzymes for the biosynthesis of auxin (IaaH, IaaM) and cytokinin (Ipt). Although these oncogenes are well studied as the tumor-inducing principle, nothing is known about the regulation of oncogene expression in plant cells. Our studies show that the intergenic regions (IGRs) between the coding sequences (CDS) of the three oncogenes function as promoters in plant cells. These promoters possess a eukaryotic sequence organization and cis-regulatory elements for the binding of plant transcription factors. WRKY18, WRKY40, WRKY60 and ARF5 were identified as activators of the Ipt promoter whereas IaaH and IaaM is constitutively expressed and no transcription factor further activates their promoters. Consistent with these results, the wrky triple mutant plants in particular, develops smaller crown galls than wild-type and exhibits a reduced Ipt transcription, despite the presence of an intact ARF5 gene. WRKY40 and WRKY60 gene expression is induced by A. tumefaciens within a few hours whereas the ARF5 gene is transcribed later during crown gall development. The WRKY proteins interact with ARF5 in the plant nucleus, but only WRKY40 together with ARF5 synergistically boosts the activation of the Ipt promoter in an auxin-dependent manner. From our data, we propose that A. tumefaciens initially induces WRKY40 gene expression as a pathogen defense response of the host cell. The WRKY protein is recruited to induce Ipt expression, which initiates cytokinin-dependent host cell division. With increasing auxin levels triggered by ubiquitous expression of IaaH and IaaM, ARF5 is activated and interacts with WRKY40 to potentiate Ipt expression and balance cytokinin and auxin levels for further cell proliferation.

Targeted proteomics analysis of protein degradation in plant signaling on an LTQ-Orbitrap mass spectrometer.

Targeted proteomics has become increasingly popular recently because of its ability to precisely quantify selected proteins in complex cellular backgrounds. Here, we demonstrated the utility of an LTQ-Orbitrap Velos Pro mass spectrometer in targeted parallel reaction monitoring (PRM) despite its unconventional dual ion trap configuration. We evaluated absolute specificity (>99%) and sensitivity (100 amol on column in 1 µg of total cellular extract) using full and mass range scans as survey scans together with data-dependent (DDA) and targeted MS/MS acquisition. The instrument duty cycle was a critical parameter limiting sensitivity, necessitating peptide retention time scheduling. We assessed synthetic peptide and recombinant peptide standards to predict or experimentally determine target peptide retention times. We applied optimized PRM to protein degradation in signaling regulation, an area that is receiving increased attention in plant physiology. We quantified relative abundance of selected proteins in plants that are mutant for enzymatic components of the N-end rule degradation (NERD) pathway such as the two tRNA-arginyl-transferases ATE1 and ATE2 and the two E3 ubiquitin ligases PROTEOLYSIS1 and 6. We found a number of upregulated proteins, which might represent degradation targets. We also targeted FLAGELLIN SENSITIVE2 (FLS2), a pattern recognition receptor responsible for pathogen sensing, in ubiquitin ligase mutants to assay the attenuation of plant immunity by degradation of the receptor.

Potential Oral Microbial Markers for Differential Diagnosis of Crohn's Disease and Ulcerative Colitis Using Machine Learning Models.

Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn's disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn's disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.

Agrobacterium tumefaciens promotes tumor induction by modulating pathogen defense in Arabidopsis thaliana.

Agrobacterium tumefaciens causes crown gall disease by transferring and integrating bacterial DNA (T-DNA) into the plant genome. To examine the physiological changes and adaptations during Agrobacterium-induced tumor development, we compared the profiles of salicylic acid (SA), ethylene (ET), jasmonic acid (JA), and auxin (indole-3-acetic acid [IAA]) with changes in the Arabidopsis thaliana transcriptome. Our data indicate that host responses were much stronger toward the oncogenic strain C58 than to the disarmed strain GV3101 and that auxin acts as a key modulator of the Arabidopsis-Agrobacterium interaction. At initiation of infection, elevated levels of IAA and ET were associated with the induction of host genes involved in IAA, but not ET signaling. After T-DNA integration, SA as well as IAA and ET accumulated, but JA did not. This did not correlate with SA-controlled pathogenesis-related gene expression in the host, although high SA levels in mutant plants prevented tumor development, while low levels promoted it. Our data are consistent with a scenario in which ET and later on SA control virulence of agrobacteria, whereas ET and auxin stimulate neovascularization during tumor formation. We suggest that crosstalk among IAA, ET, and SA balances pathogen defense launched by the host and tumor growth initiated by agrobacteria.

Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using Transcriptome Imputed from Genotypes.

Almost half of patients show no primary or secondary response to monoclonal anti-tumor necrosis factor α (anti-TNF) antibody treatment for inflammatory bowel disease (IBD). Thus, the exact mechanisms of a non-durable response (NDR) remain inadequately defined. We used our genome-wide genotype data to impute expression values as features in training machine learning models to predict a NDR. Blood samples from various IBD cohorts were used for genotyping with the Korea Biobank Array. A total of 234 patients with Crohn's disease (CD) who received their first anti-TNF therapy were enrolled. The expression profiles of 6294 genes in whole-blood tissue imputed from the genotype data were combined with clinical parameters to train a logistic model to predict the NDR. The top two and three most significant features were genetic features (DPY19L3, GSTT1, and NUCB1), not clinical features. The logistic regression of the NDR vs. DR status in our cohort by the imputed expression levels showed that the ß coefficients were positive for DPY19L3 and GSTT1, and negative for NUCB1, concordant with the known eQTL information. Machine learning models using imputed gene expression features effectively predicted NDR to anti-TNF agents in patients with CD.

Reversion of Gut Microbiota during the Recovery Phase in Patients with Asymptomatic or Mild COVID-19: Longitudinal Study.

Patients with COVID-19 have been reported to experience gastrointestinal symptoms as well as respiratory symptoms, but the effects of COVID-19 on the gut microbiota are poorly understood. We explored gut microbiome profiles associated with the respiratory infection of SARS-CoV-2 during the recovery phase in patients with asymptomatic or mild COVID-19. A longitudinal analysis was performed using the same patients to determine whether the gut microbiota changed after recovery from COVID-19. We applied 16S rRNA amplicon sequencing to analyze two paired fecal samples from 12 patients with asymptomatic or mild COVID-19. Fecal samples were selected at two time points: during SARS-CoV-2 infection (infected state) and after negative conversion of the viral RNA (recovered state). We also compared the microbiome data with those from 36 healthy controls. Microbial evenness of the recovered state was significantly increased compared with the infected state. SARS-CoV-2 infection induced the depletion of Bacteroidetes, while an abundance was observed with a tendency to rapidly reverse in the recovered state. The Firmicutes/Bacteroidetes ratio in the infected state was markedly higher than that in the recovered state. Gut dysbiosis was observed after infection even in patients with asymptomatic or mild COVID-19, while the composition of the gut microbiota was recovered after negative conversion of SARS-CoV-2 RNA. Modifying intestinal microbes in response to COVID-19 might be a useful therapeutic alternative.

Development of a Machine Learning Model to Distinguish between Ulcerative Colitis and Crohn's Disease Using RNA Sequencing Data.

Crohn's disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Differentially Abundant Bacterial Taxa Associated with Prognostic Variables of Crohn's Disease: Results from the IMPACT Study.

Limited studies have examined the intestinal microbiota composition in relation to Crohn's disease (CD) prognosis. We analyzed the differences in microbial communities and relevant metabolic pathways associated with prognostic variables in patients with CD. We applied 16S rRNA gene sequencing to analyze a cohort of 1110 CD and healthy control (HC) fecal samples. We categorized patients with CD into good (CD-G), intermediate (CD-I) and poor (CD-P) prognosis groups, according to the history of using biologics and intestinal resection. Microbiota α-diversity decreased more in CD-P than CD-G and CD-I. Microbiota ß-diversity in CD-P differed from that in CD-G and CD-I. Thirteen genera and 10 species showed differential abundance between CD-G and CD-P groups. Escherichia coli (p = 0.001) and species Producta (p = 0.01) and genera Lactobacillus (p = 0.003) and Coprococcus (p = 0.01) consistently showed differences between CD-G and CD-P groups after adjusting for confounding variables. Functional profiling suggested that the microbial catabolic pathways and pathways related to enterobacterial common antigen and lipopolysaccharide biosynthesis were better represented in the CD-P group than in the CD-G group, and E. coli were the top contributors to these pathways. CD prognosis is associated with altered microbiota composition and decreased diversity, and E. coli might be causally involved in CD progression, and may have adapted to live in inflammatory environments.

A central role of abscisic acid in drought stress protection of Agrobacterium-induced tumors on Arabidopsis.

Crown gall tumors induced by Agrobacterium tumefaciens represent a sink that has to be provided with nutrients and water by the host plant. The lack of an intact epidermis or cuticle results in uncontrolled loss of water. However, neither the tumor nor the host plant displays wilting. This phenomenon points to drought adaptation in both tumors and the crown gall host plant. To understand the underlying molecular mechanisms of protection against desiccation the gene expression pattern of Arabidopsis (Arabidopsis thaliana) tumors was integrated with the profile of stress metabolites: Arabidopsis tumors accumulated high amounts of abscisic acid (ABA), the ethylene precursor aminocyclopropyl carboxylic acid, osmoprotectants, and form a suberized periderm-like protective layer. Suberization of the outer tumor cell layers most likely is mediated by ABA since external application of ABA induced suberization of Arabidopsis roots. However, the expression level of the classical marker genes, known to respond to drought stress and/or ABA, was lower in tumors. Instead another set of drought and/or ABA-inducible genes was more highly transcribed. Elevated transcription of several ABA-dependent aquaporin genes might indicate that ABA controls the water balance of the tumor. The retarded tumor growth on abi and aba mutant plants underlined the importance of a tumor-specific ABA signaling pathway. Taken together, we propose that ABA is an important signal for protection of tumors against desiccation and thus supports tumor development.