Bacillus velezensis YC7010 Enhances Plant Defenses Against Brown Planthopper Through Transcriptomic and Metabolic Changes in Rice

Brown planthopper (BPH; Nilaparvata lugens Stål) is one of the most serious insect pests, which reduce rice yield remarkably in many rice-growing areas. A few plant growth-promoting rhizobacteria induce systemic resistance against herbivorous insects. Here we show that root drenching of rice seedlings with an endophytic strain Bacillus velezensis YC7010 enhanced defenses against BPH. Based on high-throughput transcriptome analysis, systemic resistance against BPH was induced by B. velezensis YC7010 via salicylic acid (SA)- and jasmonic acid (JA)-dependent pathways. Increased leaf contents of secondary metabolites, tricin and C-glycosyl flavone and cell-wall contents of lignin and cellulose were the key defense mechanisms inducing resistance against BPH during the three-way interaction. This study shows for the first time that chemical changes and strengthening of physical barriers play important roles simultaneously in plant defense against BPH in rice by the endophytic bacteria. This defense was induced by lipopeptides including a novel bacillopeptin X

Bloodstream Infections and Clinical Significance of Healthcare-associated Bacteremia: A Multicenter Surveillance Study in Korean Hospitals

Recent changes in healthcare systems have changed the epidemiologic paradigms in many infectious fields including bloodstream infection (BSI). We compared clinical characteristics of community-acquired (CA), hospital-acquired (HA), and healthcare-associated (HCA) BSI. We performed a prospective nationwide multicenter surveillance study from 9 university hospitals in Korea. Total 1,605 blood isolates were collected from 2006 to 2007, and 1,144 isolates were considered true pathogens. HA-BSI accounted for 48.8%, CA-BSI for 33.2%, and HCA-BSI for 18.0%. HA-BSI and HCA-BSI were more likely to have severe comorbidities. Escherichia coli was the most common isolate in CA-BSI (47.1%) and HCA-BSI (27.2%). In contrast, Staphylococcus aureus (15.2%), coagulase-negative Staphylococcus (15.1%) were the common isolates in HA-BSI. The rate of appropriate empiric antimicrobial therapy was the highest in CA-BSI (89.0%) followed by HCA-BSI (76.4%), and HA-BSI (75.0%). The 30-day mortality rate was the highest in HA-BSI (23.0%) followed by HCA-BSI (18.4%), and CA-BSI (10.2%). High Pitt score and inappropriate empirical antibiotic therapy were the independent risk factors for mortality by multivariate analysis. In conclusion, the present data suggest that clinical features, outcome, and microbiologic features of causative pathogens vary by origin of BSI. Especially, HCA-BSI shows unique clinical characteristics, which should be considered a distinct category for more appropriate antibiotic treatment

A Prediction Rule to Identify Severe Cases among Adult Patients Hospitalized with Pandemic Influenza A (H1N1) 2009

The purpose of this study was to establish a prediction rule for severe illness in adult patients hospitalized with pandemic influenza A (H1N1) 2009. At the time of initial presentation, the baseline characteristics of those with severe illness (i.e., admission to intensive care unit, mechanical ventilation, or death) were compared to those of patients with non-severe illnesses. A total of 709 adults hospitalized with pandemic influenza A (H1N1) 2009 were included: 75 severe and 634 non-severe cases. The multivariate analysis demonstrated that altered mental status, hypoxia (PaO2/FiO2 ≤ 250), bilateral lung infiltration, and old age (≥ 65 yr) were independent risk factors for severe cases (all P < 0.001). The area under the ROC curve (0.834 [95% CI, 0.778-0.890]) of the number of risk factors were not significantly different with that of APACHE II score (0.840 [95% CI, 0.790-0.891]) (P = 0.496). The presence of ≥ 2 risk factors had a higher sensitivity, specificity, positive predictive value and negative predictive value than an APACHE II score of ≥ 13. As a prediction rule, the presence of ≥ 2 these risk factors is a powerful and easy-to-use predictor of the severity in adult patients hospitalized with pandemic influenza A (H1N1) 2009

Laboratory information management system for COVID-19 non-clinical efficacy trial data

Background : As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results : In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions : This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.This research was supported by the National research foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020M3A9I2109027 and 2021M3H9A1030260)

Global co-expression network for key factor selection on environmental stress RNA-seq dataset in Capsicum annuum

Abstract Environmental stresses significantly affect plant growth, development, and productivity. Therefore, a deeper understanding of the underlying stress responses at the molecular level is needed. In this study, to identify critical genetic factors associated with environmental stress responses, the entire 737.3 Gb clean RNA-seq dataset across abiotic, biotic stress, and phytohormone conditions in Capsicum annuum was used to perform individual differentially expressed gene analysis and to construct gene co-expression networks for each stress condition. Subsequently, gene networks were reconstructed around transcription factors to identify critical factors involved in the stress responses, including the NLR gene family, previously implicated in resistance. The abiotic and biotic stress networks comprise 233 and 597 hubs respectively, with 10 and 89 NLRs. Each gene within the NLR groups in the network exhibited substantial expression to particular stresses. The integrated analysis strategy of the transcriptome network revealed potential key genes for complex environmental conditions. Together, this could provide important clues to uncover novel key factors using high-throughput transcriptome data in other species as well as plants

Genome-wide Identification, Classification, and Expression Analysis of the Receptor-Like Protein Family in Tomato

Receptor-like proteins (RLPs) are involved in plant development and disease resistance. Only some of the RLPs in tomato (Solanum lycopersicum L.) have been functionally characterized though 176 genes encoding RLPs, which have been identified in the tomato genome. To further understand the role of RLPs in tomato, we performed genome-guided classification and transcriptome analysis of these genes. Phylogenic comparisons revealed that the tomato RLP members could be divided into eight subgroups and that the genes evolved independently compared to similar genes in Arabidopsis. Based on location and physical clustering analyses, we conclude that tomato RLPs likely expanded primarily through tandem duplication events. According to tissue specific RNA-seq data, 71 RLPs were expressed in at least one of the following tissues: root, leaf, bud, flower, or fruit. Several genes had expression patterns that were tissue specific. In addition, tomato RLP expression profiles after infection with different pathogens showed distinguish gene regulations according to disease induction and resistance response as well as infection by bacteria and virus. Notably, Some RLPs were highly and/or unique expressed in susceptible tomato to pathogen, suggesting that the RLP could be involved in disease response, possibly as a host-susceptibility factor. Our study could provide an important clues for further investigations into the function of tomato RLPs involved in developmental and response to pathogens

Global co-expression network analysis for key factor selection using high-throughput environment stress responsive RNA-seq

Additional files used when generating and validating RNA-seq data Code Availability.docx --- Detailed code information used in this study</p

Genome-wide Comparative Analyses Reveal the Dynamic Evolution of Nucleotide-Binding Leucine-Rich Repeat Gene Family among Solanaceae Plants

Plants have evolved an elaborate innate immune system against invading pathogens. Within this system, intracellular nucleotide-binding leucine-rich repeat (NLR) immune receptors are known play critical roles in effector-triggered immunity (ETI) plant defense. We performed genome-wide identification and classification of NLR-coding sequences from the genomes of pepper, tomato, and potato using fixed criteria. We then compared genomic duplication and evolution features. We identified intact 267, 443, and 755 NLR-encoding genes in tomato, potato, and pepper genomes, respectively. Phylogenetic analyses and classification of Solanaceae NLRs revealed that the majority of NLR super family members fell into 14 subgroups, including a TIR-NLR (TNL) subgroup and 13 non-TNL subgroups. Specific subgroups have expanded in each genome, with the expansion in pepper showing subgroup-specific physical clusters. Comparative analysis of duplications showed distinct duplication patterns within pepper and among Solanaceae plants suggesting subgroup- or species-specific gene duplication events after speciation, resulting in divergent evolution. Taken together, genome-wide analyses of NLR family members provide insights into their evolutionary history in Solanaceae. These findings also provide important foundational knowledge for understanding NLR evolution and will empower broader characterization of disease resistance genes to be used for crop breeding

Development of Clustered Resistance Gene Analogs-Based Markers of Resistance to Phytophthora capsici in Chili Pepper

The soil-borne pathogen Phytophthora capsici causes severe destruction of Capsicum spp. Resistance in Capsicum against P. capsici is controlled by numerous minor quantitative trait loci (QTLs) and a consistent major QTL on chromosome 5. Molecular markers on Capsicum chromosome 5 have been developed to identify the predominant genetic contributor to resistance but have achieved little success. In this study, previously reported molecular markers were used to reanalyze the major QTL region on chromosome 5 (6.2 Mbp to 139.2 Mbp). Candidate resistance gene analogs (RGAs) were identified in the extended major QTL region including 14 nucleotide binding site leucine-rich repeats, 3 receptor-like kinases, and 1 receptor-like protein. Sequence comparison of the candidate RGAs was performed between two Capsicum germplasms that are resistant and susceptible, respectively, to P. capsici. 11 novel RGA-based markers were developed through high-resolution melting analysis which were closely linked to the major QTL for P. capsici resistance. Among the markers, CaNB-5480 showed the highest cosegregation rate at 86.9% and can be applied to genotyping of the germplasms that were not amenable by previous markers. With combination of three markers such as CaNB-5480, CaRP-5130 and CaNB-5330 increased genotyping accuracy for 61 Capsicum accessions. These could be useful to facilitate high-throughput germplasm screening and further characterize resistance genes against P. capsici in pepper

Prediction of disease severity using serum biomarkers in patients with mild-moderate Atopic Dermatitis: A pilot study.

Atopic dermatitis (AD) is an inflammatory skin condition that relies largely on subjective evaluation of clinical signs and symptoms for diagnosis and severity assessment. Using multivariate data, we attempted to construct prediction models that can diagnose the disease and assess its severity. We combined data from 28 mild-moderate AD patients and 20 healthy controls (HC) to create random forest models for classification (AD vs. HC) and regression analysis to predict symptom severities. The classification model outperformed the random permutation model significantly (area under the curve: 0.85 ± 0.10 vs. 0.50 ± 0.15; balanced accuracy: 0.81 ± 0.15 vs. 0.50 ± 0.15). Correlation analysis revealed a significant positive correlation between measured and predicted total SCORing Atopic Dermatitis score (SCORAD; r = 0.43), objective SCORAD (r = 0.53), eczema area and severity index scores (r = 0.58, each p < 0.001), but not between measured and predicted itch ratings (r = 0.21, p = 0.18). We developed and tested multivariate prediction models and identified important features using a variety of serum biomarkers, implying that discovering the deep-branching relationships between clinical measurements and serum measurements in mild-moderate AD patients may be possible using a multivariate machine learning method. We also suggest future methods for utilizing machine learning algorithms to enhance drug target selection, diagnosis, prognosis, and customized treatment in AD