|
1
|
Cobo-Simón M, Hart R, Ochman H. Escherichia Coli: What Is and Which Are? Mol Biol Evol 2023; 40:msac273. [PMID: 36585846 PMCID: PMC9830988 DOI: 10.1093/molbev/msac273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/11/2022] [Accepted: 12/15/2022] [Indexed: 01/01/2023] Open
Abstract
Escherichia coli have served as important model organisms for over a century-used to elucidate key aspects of genetics, evolution, molecular biology, and pathogenesis. However, defining which strains actually belong to this species is erratic and unstable due to shifts in the characters and criteria used to distinguish bacterial species. Additionally, many isolates designated as E. coli are genetically more closely related to strains of Shigella than to other E. coli, creating a situation in which the entire genus of Shigella and its four species are encompassed within the single species E. coli. We evaluated all complete genomes assigned to E. coli and its closest relatives according to the biological species concept (BSC), using evidence of reproductive isolation and gene flow (i.e., homologous recombination in the case of asexual bacteria) to ascertain species boundaries. The BSC establishes a uniform, consistent, and objective principle that allows species-level classification across all domains of life and does not rely on either phenotypic or genotypic similarity to a defined type-specimen for species membership. Analyzing a total of 1,887 sequenced genomes and comparing our results to other genome-based classification methods, we found few barriers to gene flow among the strains, clades, phylogroups, or species within E. coli and Shigella. Due to the utility in recognizing which strains constitute a true biological species, we designate genomes that form a genetic cohesive group as members of E. coliBIO.
Collapse
Affiliation(s)
- Marta Cobo-Simón
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX
| | - Rowan Hart
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX
| | - Howard Ochman
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX
| |
Collapse
|
|
2
|
Versoza CJ, Howell AA, Aftab T, Blanco M, Brar A, Chaffee E, Howell N, Leach W, Lobatos J, Luca M, Maddineni M, Mirji R, Mitra C, Strasser M, Munig S, Patel Z, So M, Sy M, Weiss S, Pfeifer SP. Comparative Genomics of Closely-Related Gordonia Cluster DR Bacteriophages. Viruses 2022; 14:1647. [PMID: 36016269 PMCID: PMC9413003 DOI: 10.3390/v14081647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/16/2022] [Accepted: 07/25/2022] [Indexed: 12/10/2022] Open
Abstract
Bacteriophages infecting bacteria of the genus Gordonia have increasingly gained interest in the scientific community for their diverse applications in agriculture, biotechnology, and medicine, ranging from biocontrol agents in wastewater management to the treatment of opportunistic pathogens in pulmonary disease patients. However, due to the time and costs associated with experimental isolation and cultivation, host ranges for many bacteriophages remain poorly characterized, hindering a more efficient usage of bacteriophages in these areas. Here, we perform a series of computational genomic inferences to predict the putative host ranges of all Gordonia cluster DR bacteriophages known to date. Our analyses suggest that BiggityBass (as well as several of its close relatives) is likely able to infect host bacteria from a wide range of genera-from Gordonia to Nocardia to Rhodococcus, making it a suitable candidate for future phage therapy and wastewater treatment strategies.
Collapse
Affiliation(s)
- Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Abigail A. Howell
- Biodesign Institute, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Tanya Aftab
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Madison Blanco
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Akarshi Brar
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Elaine Chaffee
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Nicholas Howell
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Willow Leach
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Jackelyn Lobatos
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Michael Luca
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Meghna Maddineni
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Ruchira Mirji
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Corinne Mitra
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Maria Strasser
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Saige Munig
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Zeel Patel
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Minerva So
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Makena Sy
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Sarah Weiss
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Susanne P. Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| |
Collapse
|
|
3
|
Zheng F, Li L, Zhang X, Song Y, Huang Z, Chong Y, Chen Z, Zhu H, Wu J, Chen W, Lu Y, Yang Y, Zha Y, Zhao H, Shen J. Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning. Interdiscip Sci 2021; 13:273-285. [PMID: 33641077 PMCID: PMC7914048 DOI: 10.1007/s12539-021-00420-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/22/2021] [Accepted: 02/01/2021] [Indexed: 12/23/2022]
Abstract
Abstract Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification. Graphic Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s12539-021-00420-z.
Collapse
Affiliation(s)
- Fudan Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China
| | - Ying Song
- School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Ziwang Huang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yutian Chong
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510220, China
| | - Zhiguang Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China
| | - Huiling Zhu
- College of Information Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Jiahao Wu
- School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Weifeng Chen
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China. .,National Supercomputing Center in Guangzhou, Guangzhou, 510006, China.
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Huiying Zhao
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China.
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510530, China.
| |
Collapse
|