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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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Abramov S, Boytsov A, Bykova D, Penzar DD, Yevshin I, Kolmykov SK, Fridman MV, Favorov AV, Vorontsov IE, Baulin E, Kolpakov F, Makeev VJ, Kulakovskiy IV. Landscape of allele-specific transcription factor binding in the human genome. Nat Commun 2021; 12:2751. [PMID: 33980847 PMCID: PMC8115691 DOI: 10.1038/s41467-021-23007-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/12/2021] [Indexed: 12/28/2022] Open
Abstract
Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Here the authors present a meta-analysis empowered by a new statistical method covering thousands of ChIP-Seq experiments resulting in the identification of more than 500 thousand allele-specific binding (ASB) events in the human genome.
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Affiliation(s)
- Sergey Abramov
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Alexandr Boytsov
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Daria Bykova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitry D Penzar
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Ivan Yevshin
- Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia.,Sirius University of Science and Technology, Sochi, Russia.,BIOSOFT.RU LLC, Novosibirsk, Russia
| | - Semyon K Kolmykov
- Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia.,Sirius University of Science and Technology, Sochi, Russia.,BIOSOFT.RU LLC, Novosibirsk, Russia
| | - Marina V Fridman
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Alexander V Favorov
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ilya E Vorontsov
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Eugene Baulin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,Institute of Mathematical Problems of Biology RAS-The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, Russia
| | - Fedor Kolpakov
- Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia.,Sirius University of Science and Technology, Sochi, Russia.,BIOSOFT.RU LLC, Novosibirsk, Russia
| | - Vsevolod J Makeev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia. .,Moscow Institute of Physics and Technology, Dolgoprudny, Russia. .,State Research Institute of Genetics and Selection of Industrial Microorganisms of the National Research Center Kurchatov Institute, Moscow, Russia. .,Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
| | - Ivan V Kulakovskiy
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia. .,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia. .,Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
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Konenkov VI, Lykov AP, Kabakov AV, Raiter TV, Bondarenko NA, Poveshchenko OV, Kazakov OV, Poveshchenko AF, Strunkin DN, Kolmykov SK, Chanyshev MD, Gulyaeva LF. [Associativity of microRnA levels in blood serum to quantity and functional activity of haemo - and lymphopoiesis cells in experimental breast cancer]. Vopr Onkol 2016; 62:519-524. [PMID: 30463111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The work purpose was to reveal an existence of an associativity of the microRNA levels in blood serum to quantitative and functional indices of cells haemo - and lymphopoiesis at the experimental breast cancer induced by N -methyl - N- nitrosourea in the remote period after surgery and carrying out neoadjuvant polychemotherapy. At animals there were investigated levels of microRNA-21, microRNA-221, microRNA-222 and microRNA-429 in serum, also investigated quantitative and functional parameters of cells from bone marrow, from lymph of a chest channel and from spleen. Statistically significant distinctions on the microRNA level in blood serum and an existence of interrelations of microRNA levels with quantitative and functional indices of haemo- and lymphopoiesis cells were revealed.
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