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Descr!ption of Ornithinimicrobium cryptoxanthini sp. nov., a Novel Actinomycete Producing β‑cryptoxanthin Isolated from the Tongtian River Sediments
Yuyuan Huang , Yifan Jiao , Sihui Zhang , Yuanmeihui Tao , Suping Zhang , Dong Jin , Ji Pu , Liyun Liu , Jing Yang , Shan Lu
J. Microbiol. 2023;61(4):379-388.   Published online March 16, 2023
DOI: https://doi.org/10.1007/s12275-023-00029-5
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AbstractAbstract
Two novel Gram-stain-positive, aerobic, non-motile, and yellow-pigmented, irregular rod-shaped bacteria (JY.X269 and JY.X270T) were isolated from the near-surface sediments of river in Qinghai Province, P. R. China (32°37′13″N, 96°05′37″E) in July 2019. Both strains were shown to grow at 15–35 °C and pH 7.0–10.0, and in the presence of 0–6.0% (w/v) NaCl. The 16S rRNA gene sequence analysis showed that the isolates were closely related to Ornithinimicrobium cavernae CFH 30183 T (98.6–98.8% 16S rRNA gene sequence similarity), O. ciconiae H23M54T (98.5–98.6%) and O. murale 01-Gi-040T (98.3–98.5%). The phylogenetic and phylogenomic trees based on the 16S rRNA gene and 537 core gene sequences, respectively, revealed that the two strains formed a distinct cluster with the above three species. The digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI) values between our two isolates (JY.X269 and JY.X270T) and other Ornithinimicrobium species were within the ranges of 19.0–23.9% and 70.8–80.4%, respectively, all below the respective recommended 70.0% and 95–96% cutoff point. Furthermore, the major cellular fatty acids (> 10.0%) of strains JY.X269 and JY.X270T were iso-C15:0, iso-C16:0, and summed feature 9. Strain JY.X270T contained MK-8(H4) and ornithine as the predominant menaquinone and diagnostic diamino acid component within the cell wall teichoic acids. β-cryptoxanthin ( C40H56O) can be extracted from strain JY.X270T, and its content is 6.3 μg/ml. Based on results from the phylogenetic, chemotaxonomic, and phenotypic analyses, the two strains could be classified as a novel species of the genus Ornithinimicrobium, for which the name Ornithinimicrobium cryptoxanthini sp. nov. is proposed (type strain JY.X270T = CGMCC 1.19147T = JCM 34882T).

Citations

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  • Screening, identification, and characterization of high potential bacteria for ꞵ-cryptoxanthin production from natural sources
    Sopida Korkerd, Savitri Vatanyoopaisarn, Wonnop Visessaguan, Benjawan Thumthanarak, Dudsadee Uttapap, Solange I. Mussatto, Vilai Rungsardthong
    Biocatalysis and Agricultural Biotechnology.2024; 57: 103089.     CrossRef
Review
Microbial source tracking using metagenomics and other new technologies
Shahbaz Raza , Jungman Kim , Michael J. Sadowsky , Tatsuya Unno
J. Microbiol. 2021;59(3):259-269.   Published online February 10, 2021
DOI: https://doi.org/10.1007/s12275-021-0668-9
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  • 9 Web of Science
  • 14 Crossref
AbstractAbstract
The environment is under siege from a variety of pollution sources. Fecal pollution is especially harmful as it disperses pathogenic bacteria into waterways. Unraveling origins of mixed sources of fecal bacteria is difficult and microbial source tracking (MST) in complex environments is still a daunting task. Despite the challenges, the need for answers far outweighs the difficulties experienced. Advancements in qPCR and next generation sequencing (NGS) technologies have shifted the traditional culture-based MST approaches towards culture independent technologies, where communitybased MST is becoming a method of choice. Metagenomic tools may be useful to overcome some of the limitations of community-based MST methods as they can give deep insight into identifying host specific fecal markers and their association with different environments. Adoption of machine learning (ML) algorithms, along with the metagenomic based MST approaches, will also provide a statistically robust and automated platform. To compliment that, ML-based approaches provide accurate optimization of resources. With the successful application of ML based models in disease prediction, outbreak investigation and medicine prescription, it would be possible that these methods would serve as a better surrogate of traditional MST approaches in future.

Citations

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    Sandra L. McLellan, Anthony Chariton, Annachiara Codello, Jill S. McClary-Gutierrez, Melissa K. Schussman, Ezequiel M. Marzinelli, Judith M. O’Neil, Eric J. Schott, Jennifer L. Bowen, Joe H. Vineis, Lois Maignien, Clarisse Lemonnier, Morgan Perennou, Kare
    Nature Water.2024; 2(11): 1061.     CrossRef
  • Integrating molecular microbial methods to improve faecal pollution management in rivers with designated bathing waters
    Esther Karunakaran, Rick Battarbee, Simon Tait, Bruno Melo Brentan, Cathal Berney, James Grinham, Maria Angeles Herrero, Ronex Omolo, Isabel Douterelo
    Science of The Total Environment.2024; 912: 168565.     CrossRef
  • Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants
    Zhendong Gao, Ying Lu, Mengfei Li, Yuqing Chong, Jieyun Hong, Jiao Wu, Dongwang Wu, Dongmei Xi, Weidong Deng
    International Journal of Molecular Sciences.2024; 25(17): 9271.     CrossRef
  • decOM: similarity-based microbial source tracking of ancient oral samples using k-mer-based methods
    Camila Duitama González, Riccardo Vicedomini, Téo Lemane, Nicolas Rascovan, Hugues Richard, Rayan Chikhi
    Microbiome.2023;[Epub]     CrossRef
  • Unraveling the influence of human fecal pollution on antibiotic resistance gene levels in different receiving water bodies using crAssphage indicator gene
    Zeyou Chen, Yujing Duan, Lichun Yin, Ying Chen, Yingang Xue, Xiaolong Wang, Daqing Mao, Yi Luo
    Journal of Hazardous Materials.2023; 442: 130005.     CrossRef
  • Have genetic targets for faecal pollution diagnostics and source tracking revolutionized water quality analysis yet?
    Katalin Demeter, Rita Linke, Elisenda Ballesté, Georg Reischer, René E Mayer, Julia Vierheilig, Claudia Kolm, Margaret E Stevenson, Julia Derx, Alexander K T Kirschner, Regina Sommer, Orin C Shanks, Anicet R Blanch, Joan B Rose, Warish Ahmed, Andreas H Fa
    FEMS Microbiology Reviews.2023;[Epub]     CrossRef
  • Comparative Microbial Community Analysis of Fur Seals and Aquaculture Salmon Gut Microbiomes in Tasmania
    Erin D’Agnese, Ryan J. McLaughlin, Mary-Anne Lea, Esteban Soto, Woutrina A. Smith, John P. Bowman
    Oceans.2023; 4(2): 200.     CrossRef
  • Strategies for Monitoring Microbial Life in Beach Sand for Protection of Public Health
    João Brandão, Elisabete Valério, Chelsea Weiskerger, Cristina Veríssimo, Konstantina Sarioglou, Monika Novak Babič, Helena M. Solo-Gabriele, Raquel Sabino, Maria Teresa Rebelo
    International Journal of Environmental Research and Public Health.2023; 20(9): 5710.     CrossRef
  • Microbial Source Tracking: An Emerging Technology for Microbial Water Quality Assessment: A Review
    Job, O.S., Bala, J.D., Abdulraham, A.A., Friday, N.N., Ibekie, S.A., Tsebam, C.J, Abudullahi, D.
    UMYU Journal of Microbiology Research (UJMR).2023; 8(1): 109.     CrossRef
  • Local and Environmental Reservoirs ofSalmonella entericaAfter Hurricane Florence Flooding
    Yuqing Mao, Mohamed Zeineldin, Moiz Usmani, Antarpreet Jutla, Joanna L. Shisler, Rachel J. Whitaker, Thanh H. Nguyen
    GeoHealth.2023;[Epub]     CrossRef
  • Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach
    Constanza Díaz-Gavidia, Carla Barría, Daniel L. Weller, Marilia Salgado-Caxito, Erika M. Estrada, Aníbal Araya, Leonardo Vera, Woutrina Smith, Minji Kim, Andrea I. Moreno-Switt, Jorge Olivares-Pacheco, Aiko D. Adell
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges
    James M. W. R. McElhinney, Mary Krystelle Catacutan, Aurelie Mawart, Ayesha Hasan, Jorge Dias
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • Role of gene sequencing for the diagnosis, tracking and prevention of bacterial infections
    Renu Kumari, Benu Dhawan
    Journal of The Academy of Clinical Microbiologists.2022; 24(S1): 8.     CrossRef
  • Omics-based microbiome analysis in microbial ecology: from sequences to information
    Jang-Cheon Cho
    Journal of Microbiology.2021; 59(3): 229.     CrossRef

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