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Zhelyazkova M, Yordanova R, Mihaylov I, Tsonev S, Vassilev D. In silico discovering relationship between bacteriophages and antimicrobial resistance. BIOTECHNOL BIOTEC EQ 2023. [DOI: 10.1080/13102818.2022.2151378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Maya Zhelyazkova
- Faculty of Mathematics and Informatics, Department of Probability, Operations Research and Statistics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Roumyana Yordanova
- Faculty of Science, Department of Mathematics, Hokkaido University, Sapporo, Japan
- Department of Informatics modeling, Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia, Bulgaria
| | - Iliyan Mihaylov
- Faculty of Mathematics and Informatics, Department of Information Technologies, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Stefan Tsonev
- Department of Functional Genetics, Abiotic and Biotic Stress, AgroBioInstitute, Agricultural Academy, Sofia, Bulgaria
| | - Dimitar Vassilev
- Faculty of Mathematics and Informatics, Department of Computational Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
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Hodzhev Y, Tsafarova B, Tolchkov V, Youroukova V, Ivanova S, Kostadinov D, Yanev N, Zhelyazkova M, Tsonev S, Kalfin R, Panaiotov S. Visualization of the individual blood microbiome to study the etiology of sarcoidosis. Comput Struct Biotechnol J 2023; 22:50-57. [PMID: 37928975 PMCID: PMC10624578 DOI: 10.1016/j.csbj.2023.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually. Objective To increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach. Results The 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus. Conclusions Our approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics.
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Affiliation(s)
- Yordan Hodzhev
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Borislava Tsafarova
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Vladimir Tolchkov
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Vania Youroukova
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Silvia Ivanova
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Dimitar Kostadinov
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Nikolay Yanev
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Maya Zhelyazkova
- Faculti of Mathematics and Informatics, Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria
| | - Stefan Tsonev
- Agrobioinstitute (ABI), 8 Dragan Tsankov, Blvd, Sofia 1164, Bulgaria
| | - Reni Kalfin
- Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia 1113, Bulgaria
- Department of Health Care, South-West University “Neofit Rilski”, Blagoevgrad 2700, Bulgaria
| | - Stefan Panaiotov
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
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Zhelyazkova M, Yordanova R, Mihaylov I, Kirov S, Tsonev S, Danko D, Mason C, Vassilev D. Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data. Front Genet 2021; 12:642991. [PMID: 33763122 PMCID: PMC7983949 DOI: 10.3389/fgene.2021.642991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/02/2021] [Indexed: 12/18/2022] Open
Abstract
The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.
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Affiliation(s)
- Maya Zhelyazkova
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Roumyana Yordanova
- Department of Mathematics, Hokkaido University, Sapporo, Japan.,Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia, Bulgaria
| | - Iliyan Mihaylov
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Stefan Kirov
- Bristol-Myers Squibb, Pennington, NJ, United States
| | - Stefan Tsonev
- Department of Molecular Genetics, AgroBioInstitute, Sofia, Bulgaria
| | - David Danko
- Department of Computational Informatics, Weill Cornell Medical College, New York, NY, United States
| | | | - Dimitar Vassilev
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
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