Coronafacic acid (CFA), the polyketide component of the phytotoxin coronatine (COR), is activated and coupled to coronamic acid via amide bond formation, a biosynthetic step presumably catalyzed by the CFA ligase (cfl) gene product. The COR biosynthetic gene cluster in Pseudomonas syringae pv. glycinea PG4180 is located within a 32-kb region of a 90-kb plasmid designated p4180A. In the present study, a cloned region of p4180A complemented all CFA- mutants spanning an 18.8-kb region of the COR biosynthetic cluster. The genetic evidence presented in this study indicates that cfl and the CFA biosynthetic gene cluster are encoded by a single transcript and that transcription of all of the genes in this operon is directed by the cfl promoter. The cfl promoter was localized to a 0.37-kb region upstream of the transcriptional start site by progressive subcloning in pRG960sd, a vector containing a promoterless glucuronidase gene. Transcription of the cfl/CFA operon was temperature sensitive and showed maximal glucuronidase activity at 18 degrees C. Furthermore, transcription of the cfl/CFA operon was dependent on the functional activity of a modified two-component regulatory system located within the COR biosynthetic gene cluster. Thermoregulation of the cfl/CFA operon and the coronamic acid biosynthetic gene cluster via the modified two-component regulatory system is discussed.
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http://dx.doi.org/10.1128/aem.61.11.3843-3848.1995 | DOI Listing |
Introduction: Cytomegalovirus (CMV) is a DNA-containing virus that is widespread worldwide and is of great importance in infectious pathology of children and adults. The aim of this study is to evaluate the prevalence of CMV among children and immunocompromised patients in the Nizhny Novgorod region (central Russia) and to perform a phylogenetic analysis of the identified strains.
Materials And Methods: DNA samples of CMV detected in frequently ill children and adult recipients of solid organs were studied.
Appl Microbiol Biotechnol
January 2025
State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
Identifying hormone-like quorum sensing (QS) molecules in streptomycetes is challenging due to low production levels but is essential for understanding secondary metabolite biosynthesis and morphological differentiation. This work reports the discovery of a novel γ-butenolide-type signaling molecule (SFB1) via overexpressing its biosynthetic gene (orf18) in Streptomyces fradiae. SFB1 was found to be essential for production of tylosin through dissociating the binding of its receptor TylP (a transcriptional repressor) to target genes, thus activating the expression of tylosin biosynthetic gene cluster (tyl).
View Article and Find Full Text PDFFront Plant Sci
January 2025
Institute of Food Crops, Hainan Academy of Agricultural Sciences/Hainan Key Laboratory of Crop Genetics and Breeding, Haikou, China.
Introduction: Sweet potato is an important food, feed and industrial raw material, and its tubers are rich in starch, carotenoids and anthocyanins.
Methods: To elucidate the gene expression regulation and metabolic characteristics during the development of sweet potato tubers, transcriptomic and metabolomic analyses were performed on the tubers of three different sweet potato varieties at three developmental stages (70, 100, and 130 days (d)).
Results: RNA-seq analysis revealed that 16,303 differentially expressed genes (DEGs) were divided into 12 clusters according to their expression patterns, and the pathways of each cluster were annotated.
Synth Syst Biotechnol
June 2025
Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China.
Genome mining has revealed that spp. possess numerous down-regulated or cryptic biosynthetic gene clusters (BGCs). This finding hinted that our investigation of fungal secondary metabolomes is limited.
View Article and Find Full Text PDFFront Genet
January 2025
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.
Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a , enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data.
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