Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen.

BACKGROUND: Fungi produce a wide range of specialized metabolites (SMs) involved in biotic interactions. Pathways for the production of SMs are often encoded in clusters of tightly arranged genes identified as biosynthetic gene clusters. Such gene clusters can undergo horizontal gene transfers between species and rapid evolutionary change within species. The acquisition, rearrangement, and deletion of gene clusters can generate significant metabolome diversity. However, the genetic basis underlying variation in SM production remains poorly understood. RESULTS: Here, we analyzed the metabolite production of a large population of the fungal pathogen of wheat, Zymoseptoria tritici. The pathogen causes major yield losses and shows variation in gene clusters. We performed untargeted ultra-high performance liquid chromatography-high resolution mass spectrometry to profile the metabolite diversity among 102 isolates of the same species. We found substantial variation in the abundance of the detected metabolites among isolates. Integrating whole-genome sequencing data, we performed metabolite genome-wide association mapping to identify loci underlying variation in metabolite production (i.e., metabolite-GWAS). We found that significantly associated SNPs reside mostly in coding and gene regulatory regions. Associated genes encode mainly transport and catalytic activities. The metabolite-GWAS identified also a polymorphism in the 3'UTR region of a virulence gene related to metabolite production and showing expression variation. CONCLUSIONS: Taken together, our study provides a significant resource to unravel polymorphism underlying metabolome diversity within a species. Integrating metabolome screens should be feasible for a range of different plant pathogens and help prioritize molecular studies.

Comparative study of intubating conditions and hemodynamic changes during awake fiber-optic intubation using midazolam with fentanyl versus dexmedetomidine in cases of difficult airway.

Background: The aim of the study is to compare intubating conditions and hemodynamic changes during awake fiber-optic intubation (AFOI) using midazolam and fentanyl versus dexmedetomidine in cases of difficult airway. Materials and Methods: A randomized prospective study was conducted in the department of oral and maxillofacial surgery, with a total of 60 patients, 18-55 years of age, ASA class I-II, of either sex with anticipated difficult airway planned for elective surgery. They were divided into two groups; group I patients received 1 µg/kg of dexmedetomidine and then an infusion of 0.5 to 0.7 µg/kg/hr of dexmedetomidine, whereas group II patients received 1 µg/kg of intra-venous (iv) fentanyl and 0.05 mg/kg of iv midazolam with additional doses of 0.02 mg/kg to achieve a Ramsay Sedation Scale score of ≥2. The ease of placement of the fiber-optic scope and the endotracheal tube and the patient's reaction to placement of the fiber-optic scope were assessed on a scale of 1-4 and were recorded as endoscopist satisfaction score and patient discomfort score, respectively. Results: The endoscopy time ranged from 2.66 ± 1.00 (group I) to 3.90 ± 0.96 (group II) minutes and was found to be statistically significant (p < 0.05). Also, the patient discomfort score was recorded during endoscopy (1-4) and ranged from 1.3 ± 0.53 (group I) and 2.33 ± 0.66 (group II) and was found to be statistically significant (p value < 0.05). Patients undergoing the procedure who received dexmedetomidine were thus more comfortable than those who received fentanyl and midazolam combination. Conclusion: Dexmedetomidine provided better intubating conditions, patient tolerance, higher endoscopist satisfaction, and reduced hemodynamic responses compared to fentanyl and midazolam combinations. Also, the major advantage of dexmeditomidine for preservation of airway with a lesser degree of respiratory depression allows for safer use of AFOI in cases of difficult airway.

A survey on providing customer and public administration based services using AI: chatbot.

A chatbot is emerged as an effective tool to address the user queries in automated, most appropriate and accurate way. Depending upon the complexity of the subject domain, researchers are employing variety of soft-computing techniques to make the chatbot user-friendly. It is observed that chatbots have flooded the globe with wide range of services including ordering foods, suggesting products, advising for insurance policies, providing customer support, giving financial assistance, schedule meetings etc. However, public administration based services wherein chatbot intervention influence the most, is not explored yet. This paper discuses about artificial intelligence based chatbots including their applications, challenges, architecture and models. It also talks about evolution of chatbots starting from Turing Test and Rule-based chatbots to advanced Artificial Intelligence based Chatbots (AI-Chatbots). AI-Chatbots are providing much kind of services, which this paper outlines into two main aspects including customer based services and public administration based services. The purpose of this survey is to understand and explore the possibility of customer & public administration services based chatbot. The survey demonstrates that there exist an immense potential in the AI assisted chatbot system for providing customer services and providing better governance in public administration services.

Population-level deep sequencing reveals the interplay of clonal and sexual reproduction in the fungal wheat pathogen Zymoseptoria tritici.

Pathogens cause significant challenges to global food security. On annual crops, pathogens must re-infect from environmental sources in every growing season. Fungal pathogens have evolved mixed reproductive strategies to cope with the distinct challenges of colonizing growing plants. However, how pathogen diversity evolves during growing seasons remains largely unknown. Here, we performed a deep hierarchical sampling in a single experimental wheat field infected by the major fungal pathogen Zymoseptoria tritici. We analysed whole genome sequences of 177 isolates collected from 12 distinct cultivars replicated in space at three time points of the growing season to maximize capture of genetic diversity. The field population was highly diverse with 37 SNPs per kilobase, a linkage disequilibrium decay within 200-700 bp and a high effective population size. Using experimental infections, we tested a subset of the collected isolates on the dominant cultivar planted in the field. However, we found no significant difference in virulence of isolates collected from the same cultivar compared to isolates collected on other cultivars. About 20 % of the isolate genotypes were grouped into 15 clonal groups. Pairs of clones were disproportionally found at short distances (<5 m), consistent with experimental estimates for per-generation dispersal distances performed in the same field. This confirms predominant leaf-to-leaf transmission during the growing season. Surprisingly, levels of clonality did not increase over time in the field although reproduction is thought to be exclusively asexual during the growing season. Our study shows that the pathogen establishes vast and stable gene pools in single fields. Monitoring short-term evolutionary changes in crop pathogens will inform more durable strategies to contain diseases.

A population-level invasion by transposable elements triggers genome expansion in a fungal pathogen.

Genome evolution is driven by the activity of transposable elements (TEs). The spread of TEs can have deleterious effects including the destabilization of genome integrity and expansions. However, the precise triggers of genome expansions remain poorly understood because genome size evolution is typically investigated only among deeply divergent lineages. Here, we use a large population genomics dataset of 284 individuals from populations across the globe of Zymoseptoria tritici, a major fungal wheat pathogen. We built a robust map of genome-wide TE insertions and deletions to track a total of 2456 polymorphic loci within the species. We show that purifying selection substantially depressed TE frequencies in most populations, but some rare TEs have recently risen in frequency and likely confer benefits. We found that specific TE families have undergone a substantial genome-wide expansion from the pathogen's center of origin to more recently founded populations. The most dramatic increase in TE insertions occurred between a pair of North American populations collected in the same field at an interval of 25 years. We find that both genome-wide counts of TE insertions and genome size have increased with colonization bottlenecks. Hence, the demographic history likely played a major role in shaping genome evolution within the species. We show that both the activation of specific TEs and relaxed purifying selection underpin this incipient expansion of the genome. Our study establishes a model to recapitulate TE-driven genome evolution over deeper evolutionary timescales.

Rapid sequence evolution driven by transposable elements at a virulence locus in a fungal wheat pathogen.

BACKGROUND: Plant pathogens cause substantial crop losses in agriculture production and threaten food security. Plants evolved the ability to recognize virulence factors and pathogens have repeatedly escaped recognition due rapid evolutionary change at pathogen virulence loci (i.e. effector genes). The presence of transposable elements (TEs) in close physical proximity of effector genes can have important consequences for gene regulation and sequence evolution. Species-wide investigations of effector gene loci remain rare hindering our ability to predict pathogen evolvability. RESULTS: Here, we performed genome-wide association studies (GWAS) on a highly polymorphic mapping population of 120 isolates of Zymoseptoria tritici, the most damaging pathogen of wheat in Europe. We identified a major locus underlying significant variation in reproductive success of the pathogen and damage caused on the wheat cultivar Claro. The most strongly associated locus is intergenic and flanked by genes encoding a predicted effector and a serine-type endopeptidase. The center of the locus contained a highly dynamic region consisting of multiple families of TEs. Based on a large global collection of assembled genomes, we show that the virulence locus has undergone substantial recent sequence evolution. Large insertion and deletion events generated length variation between the flanking genes by a factor of seven (5-35 kb). The locus showed also strong signatures of genomic defenses against TEs (i.e. RIP) contributing to the rapid diversification of the locus. CONCLUSIONS: In conjunction, our work highlights the power of combining GWAS and population-scale genome analyses to investigate major effect loci in pathogens.

Tackling microbial threats in agriculture with integrative imaging and computational approaches.

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

The complex genomic basis of rapid convergent adaptation to pesticides across continents in a fungal plant pathogen.

Convergent evolution leads to identical phenotypic traits in different species or populations. Convergence can be driven by standing variation allowing selection to favour identical alleles in parallel or the same mutations can arise independently. However, the molecular basis of such convergent adaptation remains often poorly resolved. Pesticide resistance in agricultural ecosystems is a hallmark of convergence in phenotypic traits. Here, we analyse the major fungal pathogen Zymoseptoria tritici causing serious losses on wheat and with fungicide resistance emergence across several continents. We sampled three population pairs each from a different continent spanning periods early and late in the application of fungicides. To identify causal loci for resistance, we combined knowledge from molecular genetics work and performed genome-wide association studies (GWAS) on a global set of isolates. We discovered yet unknown factors in azole resistance including a gene encoding membrane associated functions. We found strong support for the "hotspot" model of resistance evolution with convergent changes in a small set of loci but additional loci showed more population-specific allele frequency changes. Genome-wide scans of selection showed that half of all known resistance loci were overlapping a selective sweep region. Hence, the application of fungicides was one of the major selective agents acting on the pathogen over the past decades. Furthermore, loci identified through GWAS showed the highest overlap with selective sweep regions underlining the importance to map phenotypic trait variation in evolving populations. Our population genomic analyses highlighted that both de novo mutations and gene flow contributed to convergent pesticide adaptation.