The continued research in the isolation of novel bacterial strains is inspired by the fact that native microorganisms possess certain desired phenotypes necessary for recombinant microorganisms in the biotech industry. Most studies have focused on the isolation and characterization of strains from marine ecosystems as they present a higher microbial diversity than other sources. In this study, a marine bacterium, W5C, was isolated from red seaweed collected from Yeosu, South Korea. The isolate can utilize several natural polysaccharides such as agar, alginate, carrageenan, and chitin. Genome sequence and comparative genomics analyses suggest that strain W5C belongs to a novel species of the Cellulophaga genus, from which the name Cellulophaga omnivescoria sp. nov. is proposed. Its genome harbors 3,083 coding sequences and 146 carbohydrate-active enzymes (CAZymes). Compared to other reported Cellulophaga species, the genome of W5C contained a higher proportion of CAZymes (4.7%). Polysaccharide utilization loci (PUL) for agar, alginate, and carrageenan were identified in the genome, along with other several putative PULs. These PULs are excellent sources for discovering novel hydrolytic enzymes and pathways with unique characteristics required for biorefinery applications, particularly in the utilization of marine renewable biomass. The type strain is JCM 32108 (= KCTC 13157BP).
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http://dx.doi.org/10.1007/s00284-018-1467-3 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
The harlequin ladybird, , is a predatory beetle used globally to control pests such as aphids and scale insects. Originating from East Asia, this species has become highly invasive since its introduction in the late 19th century to Europe and North America, posing a threat to local biodiversity. Intraguild predation is hypothesized to drive the success of this invasive species, but the underlying mechanisms remain unknown.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Innovative Genomics Institute, University of California, Berkeley, CA 94720.
The widespread application of genome editing to treat and cure disease requires the delivery of genome editors into the nucleus of target cells. Enveloped delivery vehicles (EDVs) are engineered virally derived particles capable of packaging and delivering CRISPR-Cas9 ribonucleoproteins (RNPs). However, the presence of lentiviral genome encapsulation and replication proteins in EDVs has obscured the underlying delivery mechanism and precluded particle optimization.
View Article and Find Full Text PDFInt J Radiat Biol
January 2025
Chungbuk National University College of Medicine, Cheongju, Republic of Korea.
Purpose: We aimed to identify the transcriptomic signatures of soft tissue sarcoma (STS) related to radioresistance and establish a model to predict radioresistance.
Materials And Methods: Nine STS cell lines were cultured. Adenosine triphosphate-based viability was determined 5 days after irradiation with 8 Gy of X-rays in a single fraction.
PLoS Negl Trop Dis
January 2025
Laboratorio de Ingeniería Genética y Biología Celular y Molecular-Área de virus de insectos, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Quilmes, Buenos Aires, Argentina.
Mosquitoes are the primary vectors of arthropod-borne pathogens. Aedes aegypti is one of the most widespread mosquito species worldwide, responsible for transmitting diseases such as Dengue, Zika, and Chikungunya, among other medically significant viruses. Characterizing the array of viruses circulating in mosquitoes, particularly in Aedes aegypti, is a crucial tool for detecting and developing novel strategies to prevent arbovirus outbreaks.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
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