1
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Yan Z, Yeo J. Competing mechanisms in bacterial invasion of human colon mucus probed with agent-based modeling. Biophys J 2024; 123:1838-1845. [PMID: 38824388 DOI: 10.1016/j.bpj.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/28/2024] [Accepted: 05/28/2024] [Indexed: 06/03/2024] Open
Abstract
The gastrointestinal tract is inhabited by a vast community of microorganisms, termed the gut microbiota. Large colonies can pose a health threat, but the gastrointestinal mucus system protects epithelial cells from microbiota invasion. The human colon features a bilayer of mucus lining. Due to imbalances in intestinal homeostasis, bacteria may successfully penetrate the inner mucus layer, which can lead to severe gut diseases. However, it is hard to tease apart the competing mechanisms that lead to this penetration. To probe the conditions that permit bacteria penetration into the inner mucus layer, we develop an agent-based model consisting of bacteria and an inner mucus layer subject to a constant flux of nutrient fields feeding the bacteria. We find that there are three important variables that determine bacterial invasion: the bacterial reproduction rate, the contact energy between bacteria and mucus, and the rate of bacteria degrading the mucus. Under healthy conditions, all bacteria are naturally eliminated by the constant removal of mucus. In diseased states, imbalances between the rates of bacterial degradation and mucus secretion lead to bacterial invasion at certain junctures. We conduct uncertainty quantification and sensitivity analysis to compare the relative impact between these parameters. The contact energy has the strongest influence on bacterial penetration, which, in combination with bacterial degradation rate and growth rate, greatly accelerates bacterial invasion of the human gut mucus lining. Our findings will serve as predictive indicators for the etiology of intestinal diseases and highlight important considerations when developing gut therapeutics.
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Affiliation(s)
- Zhongyu Yan
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York
| | - Jingjie Yeo
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York.
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2
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Das A, Rajkhowa S, Sinha S, Zaki MEA. Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations. Comput Biol Chem 2024; 110:108048. [PMID: 38471353 DOI: 10.1016/j.compbiolchem.2024.108048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.
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Affiliation(s)
- Abhichandan Das
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam 786004, India
| | - Sanchaita Rajkhowa
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam 786004, India.
| | - Subrata Sinha
- Department of Computational Sciences, Brainware University, Barasat, Kolkata, West Bengal 700125, India
| | - Magdi E A Zaki
- Department of Chemistry, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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3
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Sato G, Kinoshita S, Yamada TG, Arai S, Kitaguchi T, Funahashi A, Doi N, Fujiwara K. Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution. ACS Synth Biol 2024; 13:1572-1581. [PMID: 38717981 DOI: 10.1021/acssynbio.4c00209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.
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Affiliation(s)
- Gaku Sato
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Saki Kinoshita
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Takahiro G Yamada
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
- Department of Molecular Biology, University of California San Diego, La Jolla, California 92093, United States
| | - Satoshi Arai
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Tetsuya Kitaguchi
- Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho, Yokohama, Kanagawa 226-8503, Japan
| | - Akira Funahashi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Nobuhide Doi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Kei Fujiwara
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
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4
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Parchem K, Letsiou S, Petan T, Oskolkova O, Medina I, Kuda O, O'Donnell VB, Nicolaou A, Fedorova M, Bochkov V, Gladine C. Oxylipin profiling for clinical research: Current status and future perspectives. Prog Lipid Res 2024; 95:101276. [PMID: 38697517 DOI: 10.1016/j.plipres.2024.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024]
Abstract
Oxylipins are potent lipid mediators with increasing interest in clinical research. They are usually measured in systemic circulation and can provide a wealth of information regarding key biological processes such as inflammation, vascular tone, or blood coagulation. Although procedures still require harmonization to generate comparable oxylipin datasets, performing comprehensive profiling of circulating oxylipins in large studies is feasible and no longer restricted by technical barriers. However, it is essential to improve and facilitate the biological interpretation of complex oxylipin profiles to truly leverage their potential in clinical research. This requires regular updating of our knowledge about the metabolism and the mode of action of oxylipins, and consideration of all factors that may influence circulating oxylipin profiles independently of the studied disease or condition. This review aims to provide the readers with updated and necessary information regarding oxylipin metabolism, their different forms in systemic circulation, the current limitations in deducing oxylipin cellular effects from in vitro bioactivity studies, the biological and technical confounding factors needed to consider for a proper interpretation of oxylipin profiles.
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Affiliation(s)
- Karol Parchem
- Department of Food Chemistry, Technology and Biotechnology, Faculty of Chemistry, Gdańsk University of Technology, 11/12 Gabriela Narutowicza St., 80-233 Gdańsk, Poland; Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 53210 Pardubice, Czech Republic.
| | - Sophia Letsiou
- Department of Biomedical Sciences, University of West Attica, Ag. Spiridonos St. Egaleo, 12243 Athens, Greece.
| | - Toni Petan
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.
| | - Olga Oskolkova
- Institute of Pharmaceutical Sciences, University of Graz, Humboldtstrasse 46/III, 8010 Graz, Austria.
| | - Isabel Medina
- Instituto de Investigaciones Marinas-Consejo Superior de Investigaciones Científicas (IIM-CSIC), Eduardo Cabello 6, E-36208 Vigo, Spain.
| | - Ondrej Kuda
- Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic.
| | - Valerie B O'Donnell
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK.
| | - Anna Nicolaou
- School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9NT, UK.
| | - Maria Fedorova
- Center of Membrane Biochemistry and Lipid Research, University Hospital and Faculty of Medicine Carl Gustav Carus of TU Dresden, 01307 Dresden, Germany.
| | - Valery Bochkov
- Institute of Pharmaceutical Sciences, University of Graz, Humboldtstrasse 46/III, 8010 Graz, Austria.
| | - Cécile Gladine
- Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France.
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5
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Aghakhani S, Niarakis A, Soliman S. MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps. J Integr Bioinform 2024; 21:jib-2023-0048. [PMID: 38314776 DOI: 10.1515/jib-2023-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/09/2024] [Indexed: 02/07/2024] Open
Abstract
Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel - European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay, Palaiseau, France
| | - Anna Niarakis
- GenHotel - European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay, Palaiseau, France
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6
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Patil N, Mirveis Z, Byrne HJ. Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays. FEBS Open Bio 2024; 14:466-486. [PMID: 38217078 PMCID: PMC10909989 DOI: 10.1002/2211-5463.13765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
This study aims to demonstrate the benefits of augmenting commercially available, real-time, in vitro glycolysis assays with phenomenological rate equation-based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell-lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two-phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway-modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end-product. This study demonstrates that augmenting a relatively simple, real-time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.
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Affiliation(s)
- Nitin Patil
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
| | - Zohreh Mirveis
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
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7
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Mandwal A, Bishop SL, Castellanos M, Westlund A, Chaconas G, Davidsen J, Lewis IA. MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data. Anal Chem 2024; 96:3382-3388. [PMID: 38359900 PMCID: PMC10902815 DOI: 10.1021/acs.analchem.3c04501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/18/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.
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Affiliation(s)
- Ayush Mandwal
- Department
of Physics and Astronomy, University of
Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Stephanie L. Bishop
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Mildred Castellanos
- Department
of Biochemistry and Molecular Biology, Cumming School of Medicine,
Snyder Institute for Chronic Diseases, University
of Calgary, 2500 University
Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Anika Westlund
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - George Chaconas
- Department
of Biochemistry and Molecular Biology, Cumming School of Medicine,
Snyder Institute for Chronic Diseases, University
of Calgary, 2500 University
Dr NW, Calgary T2N 1N4, Alberta, Canada
- Department
of Microbiology, Immunology and Infectious Diseases, Cumming School
of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Jörn Davidsen
- Department
of Physics and Astronomy, University of
Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
- Hotchkiss
Brain Institute, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Ian A. Lewis
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
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8
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Womack JA, Shah V, Audi SH, Terhune SS, Dash RK. BioModME for building and simulating dynamic computational models of complex biological systems. BIOINFORMATICS ADVANCES 2024; 4:vbae023. [PMID: 38456125 PMCID: PMC10918630 DOI: 10.1093/bioadv/vbae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/09/2024]
Abstract
Summary Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. Availability and implementation All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.
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Affiliation(s)
- Justin A Womack
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, United States
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53223, United States
| | - Viren Shah
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, United States
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53223, United States
| | - Said H Audi
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, United States
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53223, United States
| | - Scott S Terhune
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, United States
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI 53226, United States
| | - Ranjan K Dash
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, United States
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53223, United States
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, United States
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Kaya HE, Naidoo KJ. CytoCopasi: a chemical systems biology target and drug discovery visual data analytics platform. Bioinformatics 2023; 39:btad745. [PMID: 38070155 PMCID: PMC10963058 DOI: 10.1093/bioinformatics/btad745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
MOTIVATION Target discovery and drug evaluation for diseases with complex mechanisms call for a streamlined chemical systems analysis platform. Currently available tools lack the emphasis on reaction kinetics, access to relevant databases, and algorithms to visualize perturbations on a chemical scale providing quantitative details as well streamlined visual data analytics functionality. RESULTS CytoCopasi, a Maven-based application for Cytoscape that combines the chemical systems analysis features of COPASI with the visualization and database access tools of Cytoscape and its plugin applications has been developed. The diverse functionality of CytoCopasi through ab initio model construction, model construction via pathway and parameter databases KEGG and BRENDA is presented. The comparative systems biology visualization analysis toolset is illustrated through a drug competence study on the cancerous RAF/MEK/ERK pathway. AVAILABILITY AND IMPLEMENTATION The COPASI files, simulation data, native libraries, and the manual are available on https://github.com/scientificomputing/CytoCopasi.
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Affiliation(s)
- Hikmet Emre Kaya
- Department of Chemistry, Scientific Computing Research Unit, PD Hahn Building, University of Cape Town, Rondebosch 7701, South Africa
| | - Kevin J Naidoo
- Department of Chemistry, Scientific Computing Research Unit, PD Hahn Building, University of Cape Town, Rondebosch 7701, South Africa
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Ueda Y, Yanagisawa S. Transcription factor module NLP-NIGT1 fine-tunes NITRATE TRANSPORTER2.1 expression. PLANT PHYSIOLOGY 2023; 193:2865-2879. [PMID: 37595050 PMCID: PMC10663117 DOI: 10.1093/plphys/kiad458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 07/12/2023] [Accepted: 07/22/2023] [Indexed: 08/20/2023]
Abstract
Arabidopsis (Arabidopsis thaliana) high-affinity NITRATE TRANSPORTER2.1 (NRT2.1) plays a dominant role in the uptake of nitrate, the most important nitrogen (N) source for most terrestrial plants. The nitrate-inducible expression of NRT2.1 is regulated by NIN-LIKE PROTEIN (NLP) family transcriptional activators and NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR1 (NIGT1) family transcriptional repressors. Phosphorus (P) availability also affects the expression of NRT2.1 because the PHOSPHATE STARVATION RESPONSE1 transcriptional activator activates NIGT1 genes in P-deficient environments. Here, we show a biology-based mathematical understanding of the complex regulation of NRT2.1 expression by multiple transcription factors using 2 different approaches: a microplate-based assay for the real-time measurement of temporal changes in NRT2.1 promoter activity under different nutritional conditions, and an ordinary differential equation (ODE)-based mathematical modeling of the NLP- and NIGT1-regulated expression patterns of NRT2.1. Both approaches consistently reveal that NIGT1 stabilizes the amplitude of NRT2.1 expression under a wide range of nitrate concentrations. Furthermore, the ODE model suggests that parameters such as the synthesis rate of NIGT1 mRNA and NIGT1 proteins and the affinity of NIGT1 proteins for the NRT2.1 promoter substantially influence the temporal expression patterns of NRT2.1 in response to nitrate. These results suggest that the NLP-NIGT1 feedforward loop allows a precise control of nitrate uptake. Hence, this study paves the way for understanding the complex regulation of nutrient acquisition in plants, thus facilitating engineered nutrient uptake and plant response patterns using synthetic biology approaches.
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Affiliation(s)
- Yoshiaki Ueda
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences, Ohwashi 1-1, Tsukuba, Ibaraki 305-8686, Japan
- Plant Functional Biotechnology, Agro-Biotechnology Research Center, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Shuichi Yanagisawa
- Plant Functional Biotechnology, Agro-Biotechnology Research Center, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan
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11
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Khataniar A, Das A, Baruah MJ, Bania KK, Rajkhowa S, Al-Hussain SA, Zaki MEA. An Integrative Approach to Study the Inhibition of Providencia vermicola FabD Using C2-Quaternary Indolinones. Drug Des Devel Ther 2023; 17:3325-3347. [PMID: 38024529 PMCID: PMC10657194 DOI: 10.2147/dddt.s427193] [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: 06/22/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background The present study investigates the potential bioactivity of twelve experimentally designed C-2 quaternary indolinones against Providencia spp., a bacterial group of the Enterobacteriaceae family known to cause urinary tract infections. The study aims to provide insights into the bioactive properties of the investigated compounds and their potential use in developing novel treatments against Providencia spp. The experimental design of indolinones, combined with their unique chemical structure, makes them attractive candidates for further investigation. The results of this research may contribute to the development of novel therapeutic agents to combat Providencia spp. infections. Methods The synthesized indolinones (moL1-moL12) are evaluated to identify any superior activity, particularly focusing on moL12, which possesses aza functionality. The antimicrobial activities of all twelve compounds are tested in triplicates against six different Gram-positive and Gram-negative organisms, including P. vermicola (P<0.05). Computational methods have been employed to assess the pharmacokinetic properties of the compounds. Results Among the synthesized indolinones, moL12 exhibits superior activity compared to the other compounds with similar skeleton but different functional moieties. All six strains tested, including P. vermicola, demonstrated sensitivity to moL12. Computational studies support the pharmacokinetic properties of moL12, indicating acceptable absorption, distribution, metabolism, excretion, and toxicity characteristics. Conclusion Utilizing the PPI approach, we have identified a promising target, FabD, in Gram-negative bacteria. Our analysis has shown that moL12 exhibits significant potential in binding with FabD, thereby, might inhibit cell wall formation, and display superior antimicrobial activity compared to other compounds. Consequently, moL12 may be a potential therapeutic agent that could be used to combat urinary tract infections caused by Providencia spp. The findings of this research hold significant promise for the development of new and effective treatments for bacterial infections.
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Affiliation(s)
- Ankita Khataniar
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, As-786004, India
| | - Abhichandan Das
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, As-786004, India
| | - Manash J Baruah
- Department of Chemical Sciences, Tezpur University, Tezpur, As-784028, India
| | - Kusum K Bania
- Department of Chemical Sciences, Tezpur University, Tezpur, As-784028, India
| | - Sanchaita Rajkhowa
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, As-786004, India
| | - Sami A Al-Hussain
- Department of Chemistry, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Magdi E A Zaki
- Department of Chemistry, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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Scheel J, Hoch M, Wolfien M, Gupta S. NaviCenta - The disease map for placental research. Placenta 2023; 143:12-15. [PMID: 37793322 DOI: 10.1016/j.placenta.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on the interactions between tissues, cellular compartments, and molecules that mediate disease-related processes in the placenta. We built cellular and molecular interaction networks based upon manual curation and annotation of publicly available information in the scientific literature, pathways resources, and Omics data. NaviCenta (Navigate the plaCenta) serves as an open access, spatio-temporal, multi-scale knowledge base, and analytical tool for enhanced interpretation and hypothesis testing on various placental disease phenotypes.
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Affiliation(s)
- Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
| | - Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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Acencio ML, Ostaszewski M, Mazein A, Rosenstiel P, Aden K, Mishra N, Andersen V, Sidiropoulos P, Banos A, Filia A, Rahmouni S, Finckh A, Gu W, Schneider R, Satagopam V. The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. Front Immunol 2023; 14:1257321. [PMID: 38022524 PMCID: PMC10646502 DOI: 10.3389/fimmu.2023.1257321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.
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Affiliation(s)
- Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Konrad Aden
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Neha Mishra
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Vibeke Andersen
- Diagnostics and Clinical Research Unit, Institute of Regional Health Research, University Hospital of Southern Denmark, Aabenraa, Denmark
- Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Prodromos Sidiropoulos
- Rheumatology and Clinical Immunology, Medical School, University of Crete, Heraklion, Greece
- Laboratory of Rheumatology, Autoimmunity and Inflammation, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology (IMBB-FORTH), Heraklion, Greece
| | - Aggelos Banos
- Autoimmunity and Inflammation Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens and Laboratory of Molecular Hematology, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Anastasia Filia
- Autoimmunity and Inflammation Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens and Laboratory of Molecular Hematology, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute, University of Liège, Liège, Belgium
| | - Axel Finckh
- Rheumatology Division, Geneva University Hospital (HUG), Geneva, Switzerland
- Geneva Center for Inflammation Research (GCIR), University of Geneva (UNIGE), Geneva, Switzerland
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
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14
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Aghakhani S, Silva-Saffar SE, Soliman S, Niarakis A. Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts. Comput Struct Biotechnol J 2023; 21:4196-4206. [PMID: 37705596 PMCID: PMC10495551 DOI: 10.1016/j.csbj.2023.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) are amongst the key players of the tumor microenvironment (TME) and are involved in cancer initiation, progression, and resistance to therapy. They exhibit aggressive phenotypes affecting extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining a cell- and disease-specific asynchronous Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, in light of our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Sacha E Silva-Saffar
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Anna Niarakis
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
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15
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Mandwal A, Bishop SL, Castellanos M, Westlund A, Chaconas G, Lewis I, Davidsen J. Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.548964. [PMID: 37503268 PMCID: PMC10370097 DOI: 10.1101/2023.07.14.548964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms, and metabolic network modeling is commonly used to frame results in the context of a broader homeostatic system. However, network modeling of poorly characterized, non-model organisms remains challenging due to gene homology mismatches. To address this challenge, we developed Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that takes in empirical metabolomics data to refine metabolic networks for both model and unusual organisms. MINNO allows users to create and modify interactive metabolic pathway visualizations for thousands of organisms, in both individual and multi-species contexts. Herein, we demonstrate an important application of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme disease and relapsing fever. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for three Borrelia species. Using these empirically refined networks, we were able to metabolically differentiate these genetically similar species via their nucleotide and nicotinate metabolic pathways that cannot be predicted from genomic networks. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study non-model organisms.
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Affiliation(s)
- Ayush Mandwal
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| | - Stephanie L. Bishop
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Mildred Castellanos
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Anika Westlund
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - George Chaconas
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Ian Lewis
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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16
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Mendes P. Reproducibility and FAIR principles: the case of a segment polarity network model. Front Cell Dev Biol 2023; 11:1201673. [PMID: 37346177 PMCID: PMC10279958 DOI: 10.3389/fcell.2023.1201673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023] Open
Abstract
The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23 years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.
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Affiliation(s)
- Pedro Mendes
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT, United States
- Department of Cell Biology, University of Connecticut School of Medicine, Farmington, CT, United States
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17
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Nath SK, Pankajakshan P, Sharma T, Kumari P, Shinde S, Garg N, Mathur K, Arambam N, Harjani D, Raj M, Kwatra G, Venkatesh S, Choudhoury A, Bano S, Tayal P, Sharan M, Arora R, Strych U, Hotez PJ, Bottazzi ME, Rawal K. A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets. Vaccines (Basel) 2023; 11:vaccines11020267. [PMID: 36851145 PMCID: PMC9963959 DOI: 10.3390/vaccines11020267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/28/2023] Open
Abstract
Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.
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Affiliation(s)
- Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Preeti Pankajakshan
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Priya Kumari
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Sweety Shinde
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Nikita Garg
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Kartavya Mathur
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Nevidita Arambam
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Divyank Harjani
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Manpriya Raj
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Garwit Kwatra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Sayantan Venkatesh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Alakto Choudhoury
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Saima Bano
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Prashansa Tayal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Mahek Sharan
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Ruchika Arora
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Ulrich Strych
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peter J. Hotez
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Maria Elena Bottazzi
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Kamal Rawal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
- Correspondence:
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18
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Bhandari G, Sharma M, Negi S, Gangola S, Bhatt P, Chen S. System biology analysis of endosulfan biodegradation in bacteria and its effect in other living systems: modeling and simulation studies. J Biomol Struct Dyn 2022; 40:13171-13183. [PMID: 34622744 DOI: 10.1080/07391102.2021.1982773] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Endosulfan is a broadly applied cyclodiene insecticide which has been in use across 80 countries since last 5 decades. Owing to its recalcitrant nature, endosulfan residues have been reported from air, water and soil causing toxicity to various non-target organisms. Microbial decontamination of endosulfan has been reported previously by several authors. In the current study, we have evaluated the pathways of endosulfan degradation and its hazardous impact on other living beings including insects, humans, plants, aquatic life and environment by in-silico methods. For establishment of the endosulfan metabolism in different ecosystems, cell designer was employed. The established model was thereafter assessed and simulated to understand the biochemical and physiological metabolism of the endosulfan in various systems of the network. Topological investigation analysis of the endosulfan metabolism validated the presence of 207 nodes and 274 edges in the network. We have concluded that biomagnification of the endosulfan generally occurs in the various elements of the ecosystem. Dynamics study of endosulfan degrading enzymes suggested the important role of monooxygenase I, II and hydrolase in endosulfan bioremediation. Endosulfan shows toxicity in human beings, fishes and plants, however it is biodegraded by the microbes. To date, there are no reports of in- silico analysis of bioremediation of endosulfan and its hazardous effects on the environment. Thus, this report can be important in terms of modelling and simulation of biodegradation network of endosulfan and similar compounds and their impact on several other systems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Geeta Bhandari
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Mukund Sharma
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Shalini Negi
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Saurabh Gangola
- School of Agriculture, Graphic Era Hill University, Bhimtal Campus, Uttarakhand, India
| | - Pankaj Bhatt
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
| | - Shaohua Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
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19
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Kosić M, Benković M, Jurina T, Valinger D, Gajdoš Kljusurić J, Tušek AJ. Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis. Nutrients 2022; 14:nu14234992. [PMID: 36501022 PMCID: PMC9740596 DOI: 10.3390/nu14234992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model's dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (GB) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (tf = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.
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20
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SyBLaRS: A web service for laying out, rendering and mining biological maps in SBGN, SBML and more. PLoS Comput Biol 2022; 18:e1010635. [DOI: 10.1371/journal.pcbi.1010635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/28/2022] [Accepted: 10/04/2022] [Indexed: 11/15/2022] Open
Abstract
Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support.
To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of well-known graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest.
We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools.
SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.
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21
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Hoch M, Ehlers L, Bannert K, Stanke C, Brauer D, Caton V, Lamprecht G, Wolkenhauer O, Jaster R, Wolfien M. In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia. Front Nutr 2022; 9:989453. [DOI: 10.3389/fnut.2022.989453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022] Open
Abstract
Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.
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22
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Schnitzer B, Österberg L, Skopa I, Cvijovic M. Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing. PLoS Comput Biol 2022; 18:e1010261. [PMID: 35797415 PMCID: PMC9295998 DOI: 10.1371/journal.pcbi.1010261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/19/2022] [Accepted: 05/31/2022] [Indexed: 11/18/2022] Open
Abstract
The accumulation of protein damage is one of the major drivers of replicative ageing, describing a cell’s reduced ability to reproduce over time even under optimal conditions. Reactive oxygen and nitrogen species are precursors of protein damage and therefore tightly linked to ageing. At the same time, they are an inevitable by-product of the cell’s metabolism. Cells are able to sense high levels of reactive oxygen and nitrogen species and can subsequently adapt their metabolism through gene regulation to slow down damage accumulation. However, the older or damaged a cell is the less flexibility it has to allocate enzymes across the metabolic network, forcing further adaptions in the metabolism. To investigate changes in the metabolism during replicative ageing, we developed an multi-scale mathematical model using budding yeast as a model organism. The model consists of three interconnected modules: a Boolean model of the signalling network, an enzyme-constrained flux balance model of the central carbon metabolism and a dynamic model of growth and protein damage accumulation with discrete cell divisions. The model can explain known features of replicative ageing, like average lifespan and increase in generation time during successive division, in yeast wildtype cells by a decreasing pool of functional enzymes and an increasing energy demand for maintenance. We further used the model to identify three consecutive metabolic phases, that a cell can undergo during its life, and their influence on the replicative potential, and proposed an intervention span for lifespan control.
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Affiliation(s)
- Barbara Schnitzer
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Linnea Österberg
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Iro Skopa
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Marija Cvijovic
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
- * E-mail:
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23
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Heydenreich FM, Plouffe B, Rizk A, Milic D, Zhou J, Breton B, Le Gouill C, Inoue A, Bouvier M, Veprintsev D. Michaelis-Menten quantification of ligand signalling bias applied to the promiscuous Vasopressin V2 receptor. Mol Pharmacol 2022; 102:139-149. [PMID: 35779859 DOI: 10.1124/molpharm.122.000497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/06/2022] [Indexed: 11/22/2022] Open
Abstract
Activation of the G protein-coupled receptors by agonists may result in the activation of one or more G proteins and recruitment of arrestins. The extent of the activation of each of these pathways depends on the intrinsic efficacy of the ligand. Quantification of intrinsic efficacy relative to a reference compound is essential for the development of novel compounds. In the operational model, changes in efficacy can be compensated by changes in the "functional" affinity, resulting in poorly defined values. To separate the effects of ligand affinity from the intrinsic activity of the receptor, we developed a Michaelis-Menten based quantification of G protein activation bias that uses experimentally measured ligand affinities and provides a single measure of ligand efficacy. We used it to evaluate the signalling of a promiscuous model receptor, the Vasopressin V2 receptor (V2R). Using BRET-based biosensors, we show that the V2R engages many different G proteins across all G protein subfamilies in response to its primary endogenous agonist, arginine vasopressin (AVP), including Gs and members of the Gi/o and G12/13 families. These signaling pathways are also activated by the synthetic peptide desmopressin, oxytocin, and the non-mammalian hormone vasotocin. We compared bias quantification using the operational model with Michaelis-Menten based quantification, the latter accurately quantified ligand efficacies despite large difference in ligand affinities. Together, these results showed that V2R is promiscuous in its ability to engage several G proteins and that its' signaling profile is biased by small structural changes in the ligand. Significance Statement By modelling the G protein activation as Michaelis-Menten reaction, we developed a novel way of quantifying signalling bias. V2R activates or at least engages G proteins from all G protein subfamilies, including Gi2, Gz, Gq, G12, and G13. Their relative activation may explain its Gs-independent signalling.
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Affiliation(s)
| | - Bianca Plouffe
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, United Kingdom
| | | | - Dalibor Milic
- Department of Structural and Computational Biology, University of Vienna, Austria
| | - Joris Zhou
- Institute for Research in Immunology and Cancer, University of Montreal, Canada
| | - Billy Breton
- Institute for Research in Immunology and Cancer, University of Montreal, Canada
| | | | | | - Michel Bouvier
- Department of Biochemistry and Molec ular Medicine, University of Montreal, Canada
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24
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Genome analysis uncovers the prolific antagonistic and plant growth-promoting potential of endophyte Bacillus velezensis K1. Gene 2022; 836:146671. [PMID: 35714801 DOI: 10.1016/j.gene.2022.146671] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 05/23/2022] [Accepted: 06/10/2022] [Indexed: 11/24/2022]
Abstract
Insights into the application of endophytic bacilli in sustainable agricultural practices have opened up new avenues for the inhibition of soil-borne pathogens and the improvement of plant health. Bacillus subtilis K1, an endophytic bacterium originally isolated from aerial roots of Ficus benghalensis is a potential biocontrol agent secreting a mixture of surfactins, iturins and fengycins. The current study extends the characterization of this bacterium through genomic and comparative genomics approaches. The sequencing of the bacterial genome at Illumina MiSeq platform revealed that it possessed a 4,103,502-bp circular chromosome with 45.98% GC content and 4325 predicted protein-coding sequences. Based on phylogenomics and whole-genome average nucleotide identity, the B. subtilis K1 was taxonomically classified as Bacillus velezensis. The formerly evaluated phenotypic traits viz. C-source utilization and lipopeptide-mediated fungal antagonism were correlated to their molecular determinants. The genome also harbored several genes associated with induced systemic resistance and plant growth promotion i.e, phytohormone production, nitrogen assimilation and reduction, siderophore production, phosphate solubilization, biofilm formation, swarming motility, acetoin and butanediol synthesis. The production of antifungal volatile organic compounds and plant growth promotion was experimentally demonstrated by volatile compound assay and seed germination assay on cumin and groundnut. The isolate also holds great prospects for application as a soil inoculant as indicated by enhancement in the growth of groundnut via in planta pot studies. Bacterial pan-genome analysis based on a comparison of whole genomes with eighteen other Bacillus strains was also conducted. Comparative examination of biosynthetic gene clusters across all genomes indicated that the largest number of gene clusters were harbored by the K1 genome. Based on the findings, we propose K1 as a model for scrutinizing non-ribosomally synthesized peptide synthetase and polyketide synthetase derived molecules.
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25
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Pathak RK, Singh DB, Singh R. Introduction to basics of bioinformatics. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00006-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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26
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Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L, Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci 2022; 31:8-22. [PMID: 34717010 PMCID: PMC8740835 DOI: 10.1002/pro.4218] [Citation(s) in RCA: 488] [Impact Index Per Article: 244.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/24/2021] [Accepted: 10/26/2021] [Indexed: 02/03/2023]
Abstract
Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed carefully in order to avoid systematic errors and artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org) is a publicly available, user-focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline that includes computational and manual processes and quality control steps. First, fully reconciled phylogenetic trees (including ancestral protein sequences) are reconstructed for a set of "reference" protein sequences obtained from fully sequenced genomes of organisms across the tree of life. Second, the resulting phylogenetic trees are manually reviewed and annotated with function evolution events: inferred gains and losses of protein function along branches of the phylogenetic tree. Here, we describe in detail the current contents of PANTHER, how those contents are generated, and how they can be used in a variety of applications. The PANTHER knowledgebase can be downloaded or accessed via an extensive API. In addition, PANTHER provides software tools to facilitate the application of the knowledgebase to common protein sequence analysis tasks: exploring an annotated genome by gene function; performing "enrichment analysis" of lists of genes; annotating a single sequence or large batch of sequences by homology; and assessing the likelihood that a genetic variant at a particular site in a protein will have deleterious effects.
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Affiliation(s)
- Paul D. Thomas
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dustin Ebert
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Anushya Muruganujan
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Tremayne Mushayahama
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Laurent‐Philippe Albou
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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27
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Lakhova TN, Kazantsev FV, Lashin SA, Matushkin YG. The finding and researching algorithm for potentially oscillating enzymatic systems. Vavilovskii Zhurnal Genet Selektsii 2021; 25:318-330. [PMID: 34901728 PMCID: PMC8627878 DOI: 10.18699/vj21.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 11/19/2022] Open
Abstract
Many processes in living organisms are subject to periodic oscillations at different hierarchical levels of their organization: from molecular-genetic to population and ecological. Oscillatory processes are responsible for cell cycles in both prokaryotes and eukaryotes, for circadian rhythms, for synchronous coupling of respiration with cardiac contractions, etc. Fluctuations in the numbers of organisms in natural populations can be caused by the populations' own properties, their age structure, and ecological relationships with other species. Along with experimental approaches, mathematical and computer modeling is widely used to study oscillating biological systems. This paper presents classical mathematical models that describe oscillatory behavior in biological systems. Methods for the search for oscillatory molecular-genetic systems are presented by the example of their special case - oscillatory enzymatic systems. Factors influencing the cyclic dynamics in living systems, typical not only of the molecular-genetic level, but of higher levels of organization as well, are considered. Application of different ways to describe gene networks for modeling oscillatory molecular-genetic systems is considered, where the most important factor for the emergence of cyclic behavior is the presence of feedback. Techniques for finding potentially oscillatory enzymatic systems are presented. Using the method described in the article, we present and analyze, in a step-by-step manner, first the structural models (graphs) of gene networks and then the reconstruction of the mathematical models and computational experiments with them. Structural models are ideally suited for the tasks of an automatic search for potential oscillating contours (linked subgraphs), whose structure can correspond to the mathematical model of the molecular-genetic system that demonstrates oscillatory behavior in dynamics. At the same time, it is the numerical study of mathematical models for the selected contours that makes it possible to confirm the presence of stable limit cycles in them. As an example of application of the technology, a network of 300 metabolic reactions of the bacterium Escherichia coli was analyzed using mathematical and computer modeling tools. In particular, oscillatory behavior was shown for a loop whose reactions are part of the tryptophan biosynthesis pathway.
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Affiliation(s)
- T N Lakhova
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia
| | - F V Kazantsev
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia
| | - S A Lashin
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - Yu G Matushkin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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28
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Sasidharakurup H, Kumar G, Nair B, Diwakar S. Mathematical Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Network with Cytokine Storm, Oxidative Stress, Thrombosis, Insulin Resistance, and Nitric Oxide Pathways. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 25:770-781. [PMID: 34807729 DOI: 10.1089/omi.2021.0155] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a systemic disease affecting not only the lungs but also multiple organ systems. Clinical studies implicate that SARS-CoV-2 infection causes imbalance of cellular homeostasis and immune response that trigger cytokine storm, oxidative stress, thrombosis, and insulin resistance. Mathematical modeling can offer in-depth understanding of the SARS-CoV-2 infection and illuminate how subcellular mechanisms and feedback loops underpin disease progression and multiorgan failure. We report here a mathematical model of SARS-CoV-2 infection pathway network with cytokine storm, oxidative stress, thrombosis, insulin resistance, and nitric oxide (NO) pathways. The biochemical systems theory model shows autocrine loops with positive feedback enabling excessive immune response, cytokines, transcription factors, and interferons, which can imbalance homeostasis of the system. The simulations suggest that changes in immune response led to uncontrolled release of cytokines and chemokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor α (TNFα), and affect insulin, coagulation, and NO signaling pathways. Increased production of NETs (neutrophil extracellular traps), thrombin, PAI-1 (plasminogen activator inhibitor-1), and other procoagulant factors led to thrombosis. By analyzing complex biochemical reactions, this model forecasts the key intermediates, potential biomarkers, and risk factors at different stages of COVID-19. These insights can be useful for drug discovery and development, as well as precision treatment of multiorgan implications of COVID-19 as seen in systems medicine.
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Affiliation(s)
- Hemalatha Sasidharakurup
- Amrita Mind Brain Center and Amrita Vishwa Vidyapeetham, Kollam, India
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Geetha Kumar
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
- Tata Institute for Genetics and Society, Kodigehalli, Bengaluru, India
| | - Bipin Nair
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
- Tata Institute for Genetics and Society, Kodigehalli, Bengaluru, India
| | - Shyam Diwakar
- Amrita Mind Brain Center and Amrita Vishwa Vidyapeetham, Kollam, India
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
- School of Engineering, Amrita Vishwa Vidyapeetham, Kollam, India
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29
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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30
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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31
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SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models. Processes (Basel) 2021. [DOI: 10.3390/pr9101830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.
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32
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Sekiguchi T, Hamada H, Okamoto M. WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways. Bioengineering (Basel) 2021; 8:bioengineering8080114. [PMID: 34436117 PMCID: PMC8389272 DOI: 10.3390/bioengineering8080114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/01/2021] [Accepted: 08/05/2021] [Indexed: 11/16/2022] Open
Abstract
We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.
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Affiliation(s)
- Tatsuya Sekiguchi
- Department of Life Sciences and Informatics, Faculty of Engineering, Maebashi Institute of Technology, 460-1, Kamisatori-cho, Maebashi 371-0816, Japan
- Correspondence:
| | - Hiroyuki Hamada
- Department of Bioscience and Biotechnology, Faculty of Agriculture, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan;
| | - Masahiro Okamoto
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan;
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33
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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34
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CFTR Lifecycle Map-A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations. Int J Mol Sci 2021; 22:ijms22147590. [PMID: 34299207 PMCID: PMC8306775 DOI: 10.3390/ijms22147590] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
Abstract
Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics.
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35
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Balci H, Siper MC, Saleh N, Safarli I, Roy L, Kilicarslan M, Ozaydin R, Mazein A, Auffray C, Babur Ö, Demir E, Dogrusoz U. Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps. Bioinformatics 2021; 37:1475-1477. [PMID: 33010165 DOI: 10.1093/bioinformatics/btaa850] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/25/2020] [Accepted: 09/18/2020] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. RESULTS We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. AVAILABILITY AND IMPLEMENTATION Newt's source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.
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Affiliation(s)
- Hasan Balci
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Metin Can Siper
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.,Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Nasim Saleh
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Ilkin Safarli
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.,Visualization Design Lab, School of Computing, University of Utah, Salt Lake City, UT 84112, USA
| | - Ludovic Roy
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Merve Kilicarslan
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Rumeysa Ozaydin
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Özgün Babur
- Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA.,Computer Science Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Emek Demir
- Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ugur Dogrusoz
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
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36
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Dato S, Crocco P, Rambaldi Migliore N, Lescai F. Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging. Front Genet 2021; 12:689824. [PMID: 34178042 PMCID: PMC8225294 DOI: 10.3389/fgene.2021.689824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022] Open
Abstract
Background Aging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration. Recent Advances In this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today. Critical Issues Although the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types. Future Directions We critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.
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Affiliation(s)
- Serena Dato
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Paolina Crocco
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | | | - Francesco Lescai
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy
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Prugger M, Einkemmer L, Beik SP, Wasdin PT, Harris LA, Lopez CF. Unsupervised logic-based mechanism inference for network-driven biological processes. PLoS Comput Biol 2021; 17:e1009035. [PMID: 34077417 PMCID: PMC8202945 DOI: 10.1371/journal.pcbi.1009035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/14/2021] [Accepted: 05/03/2021] [Indexed: 01/21/2023] Open
Abstract
Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.
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Affiliation(s)
- Martina Prugger
- Department of Biochemistry, University of Innsbruck, Innsbruck, Austria
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Lukas Einkemmer
- Department of Mathematics, University of Innsbruck, Innsbruck, Austria
| | - Samantha P. Beik
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Perry T. Wasdin
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, United States of America
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, Arkansas, United States of America
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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38
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Chowdhury S, Hepper S, Lodi MK, Saier MH, Uetz P. The Protein Interactome of Glycolysis in Escherichia coli. Proteomes 2021; 9:proteomes9020016. [PMID: 33917325 PMCID: PMC8167557 DOI: 10.3390/proteomes9020016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/31/2021] [Accepted: 04/02/2021] [Indexed: 11/16/2022] Open
Abstract
Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284, USA; or
| | - Stephen Hepper
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
| | - Mudassir K. Lodi
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
| | - Milton H. Saier
- Department of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093, USA;
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA; (S.H.); (M.K.L.)
- Correspondence:
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39
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Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics 2021; 36:4473-4482. [PMID: 32403123 PMCID: PMC7575051 DOI: 10.1093/bioinformatics/btaa484] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/17/2020] [Accepted: 05/06/2020] [Indexed: 12/16/2022] Open
Abstract
Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sara Sadat Aghamiri
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Vidisha Singh
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Aurélien Naldi
- Département de Biologie, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ècole Normale Supérieure, CNRS, INSERM, Université PSL, Paris 75005, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Anna Niarakis
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
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40
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Martens M, Ammar A, Riutta A, Waagmeester A, Slenter D, Hanspers K, A. Miller R, Digles D, Lopes E, Ehrhart F, Dupuis LJ, Winckers LA, Coort S, Willighagen EL, Evelo CT, Pico AR, Kutmon M. WikiPathways: connecting communities. Nucleic Acids Res 2021; 49:D613-D621. [PMID: 33211851 PMCID: PMC7779061 DOI: 10.1093/nar/gkaa1024] [Citation(s) in RCA: 455] [Impact Index Per Article: 151.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | | | - Denise N Slenter
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Ryan A. Miller
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Chemistry/Pharmacoinformatics Research Group, University of Vienna, 1090 Vienna, Austria
| | - Elisson N Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Lauren J Dupuis
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Laurent A Winckers
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Susan L Coort
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Martina Kutmon
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
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41
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Role of Bioinformatics in Biological Sciences. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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42
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Monraz Gomez LC, Kondratova M, Sompairac N, Lonjou C, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Atlas of Cancer Signaling Network: A Resource of Multi-Scale Biological Maps to Study Disease Mechanisms. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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43
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Mi H, Ebert D, Muruganujan A, Mills C, Albou LP, Mushayamaha T, Thomas PD. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res 2020; 49:D394-D403. [PMID: 33290554 PMCID: PMC7778891 DOI: 10.1093/nar/gkaa1106] [Citation(s) in RCA: 774] [Impact Index Per Article: 193.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/19/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023] Open
Abstract
PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.
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Affiliation(s)
- Huaiyu Mi
- Correspondence may also be addressed to Huaiyu Mi.
| | - Dustin Ebert
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Anushya Muruganujan
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Caitlin Mills
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Laurent-Philippe Albou
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Tremayne Mushayamaha
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Paul D Thomas
- To whom correspondence should be addressed. Tel: +1 323 442 7975;
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44
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Bhatt P, Sethi K, Gangola S, Bhandari G, Verma A, Adnan M, Singh Y, Chaube S. Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems. J Biomol Struct Dyn 2020; 40:3285-3295. [PMID: 33179575 DOI: 10.1080/07391102.2020.1846623] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Atrazine is the most commonly used herbicide worldwide in the agricultural system. The increased environmental concentration of the atrazine showed the toxic effects on the non-target living species. Biodegradation of the atrazine is possible with the bacterial systems. The present study investigated biodegradation potential of atrazine degrading bacteria and the impact of atrazine on environmental systems. Model of atrazine fate in ecological systems constructed using the cell designer. The used model further analyzed and simulated to know the biochemistry and physiology of the atrazine in different cellular networks. Topological analysis of the atrazine degradation confirmed the 289 nodes and 300 edges. Our results showed that the overall biomagnification of the atrazine in the different environmental systems. Atrazine is showing toxic effects on humans and plants, whereas degraded by the bacterial systems. To date, no one has analyzed the complete degradation and poisonous effects of the atrazine in the environment. Therefore, this study is useful for overall system biology based modeling and simulation analysis of atrazine in living systems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Pankaj Bhatt
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Kanika Sethi
- Department of Microbiology, Dolphin (P.G) Institute of Biomedical and Natural Sciences, Dehradun, India
| | - Saurabh Gangola
- School of Agriculture, Graphic Era Hill University Bhimtal Campus, Uttarakhand, India
| | - Geeta Bhandari
- Department of Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Amit Verma
- Department of Biochemistry, College of Basic Science and Humanities, SD Agricultural University, Gujarat, India
| | - Muhammad Adnan
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Yashpal Singh
- Department of Veterinary Physiology and Biochemistry, G.B Pant University of Agriculture and Technology, Pantnagar, India
| | - Shshank Chaube
- Department of Mathematics, University of Petrolium and Energy Studies, Dehradun, India
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Sekiguchi T, Hamada H, Okamoto M. WinBEST-KIT: Biochemical reaction simulator that can define and customize algebraic equations and events as GUI components. J Bioinform Comput Biol 2020; 17:1950036. [PMID: 32019416 DOI: 10.1142/s0219720019500367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We previously developed Windows-based Biochemical Engineering System analyzing Tool-KIT (WinBEST-KIT), a biochemical reaction simulator for analyzing large-scale and complicated biochemical reaction networks. One particularly notable feature is the ability for users to define original mathematical equations for representing unknown kinetic mechanisms and customize them as GUI components for representing reaction steps. Many simulators support System Biology Markup Language SBML; however, since the definition of the algebraic equations (AssignmentRule) and the events are made through an interface that is distinct from the definition of the reaction steps, there are tough works to define them. Accordingly, we have developed a new version of WinBEST-KIT that allows users to define the algebraic equations and the events through the same interface as those used in the definition of the reaction steps and customize them as GUI components appearing in the symbol selection area. The customized algebraic equations and events can thus be visually arranged at any time and any place. It also allows users to easily understand the roles of the algebraic equations and the events. We have also implemented other useful features, including importing/exporting of SBML format files, exporting to MATLAB, and merging the existing models into the model currently being created. The current version of WinBEST-KIT is freely available at http://winbest-kit.org/.
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Affiliation(s)
- Tatsuya Sekiguchi
- Department of Life Sciences and Informatics, Faculty of Engineering, Maebashi Institute of Technology, 460-1, Kamisatori-cho, Maebashi, Gunma 371-0816, Japan
| | - Hiroyuki Hamada
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Masahiro Okamoto
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
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Bhatt K, Maheshwari DK. Insights into zinc-sensing metalloregulator 'Zur' deciphering mechanism of zinc transportation in Bacillus spp. by modeling, simulation and molecular docking. J Biomol Struct Dyn 2020; 40:764-779. [PMID: 32924811 DOI: 10.1080/07391102.2020.1818625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
To comprehend the molecular mechanism of zinc transportation by bacteria tends to be a very complicated and time-consuming method. To date, fragmented and scanty studies are available about the mechanism of zinc transportation at molecular level. So, the present study scrutinizes in silico pathways of zinc fractions transportation, specifically in Bacillus spp. stimulating dynamic performance of zinc. For this, the constructed model reveals Zur to be the prime regulatory transport protein maintaining bacterial survivability at fluctuation in zinc concentrations, thereby attaining zinc homeostasis. Topology for hub nodes displays appropriate evidence of the molecular basis of bacterial zinc imports and exports. Further, the molecular docking reveals interaction of Zur protein with the zinc ligands (ZnCO3 and ZnSO4). By validation of binding affinity, binding energy and docking score via Autodock Vina and X-Score, the ZnSO4 compound was found to possess excellent stability in the active pocket site of Zur, stating Zur-ZnSO4 complex to be the most potential. Owing to which, the Zur-ZnSO4 complex was selected and subjected to molecular dynamics simulation, revealing RMSD, RG, RMSF, SASA and interaction energy for 20 ns trajectory period. Henceforth,the study provides novel insight into revealing the unrecognized Zur protein pathway, assisting zinc transportation, besides retaining best interaction with ZnSO4 ligand. This is the first system biology where molecular docking and molecular dynamics simulation-based investigation decipher the role of Zur transport protein system and interaction of its amino acids with zinc ligands in a simpler and economical form via in silico techniques.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kalpana Bhatt
- Department of Botany and Microbiology, Gurukula Kangri University, Haridwar, Uttarakhand, India
| | - Dinesh Kumar Maheshwari
- Department of Botany and Microbiology, Gurukula Kangri University, Haridwar, Uttarakhand, India
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47
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Piereck B, Oliveira-Lima M, Benko-Iseppon AM, Diehl S, Schneider R, Brasileiro-Vidal AC, Barbosa-Silva A. LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks. BMC Bioinformatics 2020; 21:365. [PMID: 32838742 PMCID: PMC7447576 DOI: 10.1186/s12859-020-03620-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 06/19/2020] [Indexed: 11/11/2022] Open
Abstract
Background The amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR’s native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied. Results By taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 co-occurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response. Conclusions LAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.
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Affiliation(s)
- Bruna Piereck
- Genetics Department, Laboratório de Genética e Biologia Vegetal, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
| | - Marx Oliveira-Lima
- Genetics Department, Laboratório de Genética e Biologia Vegetal, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
| | - Ana Maria Benko-Iseppon
- Genetics Department, Laboratório de Genética e Biologia Vegetal, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
| | - Sarah Diehl
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Bioinformatics Core, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Bioinformatics Core, Esch-sur-Alzette, Luxembourg
| | - Ana Christina Brasileiro-Vidal
- Genetics Department, Laboratório de Genética e Biologia Vegetal, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
| | - Adriano Barbosa-Silva
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Bioinformatics Core, Esch-sur-Alzette, Luxembourg. .,Queen Mary University of London, Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Charterhouse Square, London, UK.
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48
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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49
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Hoksza D, Gawron P, Ostaszewski M, Hasenauer J, Schneider R. Closing the gap between formats for storing layout information in systems biology. Brief Bioinform 2020; 21:1249-1260. [PMID: 31273380 PMCID: PMC7373180 DOI: 10.1093/bib/bbz067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/23/2019] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.
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Affiliation(s)
- David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00 Prague, Czech Republic
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, München, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
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50
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Effects of repetitive Iodine thyroid blocking on the foetal brain and thyroid in rats: a systems biology approach. Sci Rep 2020; 10:10839. [PMID: 32616734 PMCID: PMC7331645 DOI: 10.1038/s41598-020-67564-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 06/03/2020] [Indexed: 12/20/2022] Open
Abstract
A single administration of an iodine thyroid blocking agent is usually sufficient to protect thyroid from radioactive iodine and prevent thyroid cancer. Repeated administration of stable iodine (rKI) may be necessary during prolonged or repeated exposure to radioactive iodine. We previously showed that rKI for eight days offers protection without toxic effects in adult rats. However, the effect of rKI administration in the developing foetus is unknown, especially on brain development, although a correlation between impaired maternal thyroid status and a decrease in intelligence quotient of the progeny has been observed. This study revealed distinct gene expression profiles between the progeny of rats receiving either rKI or saline during pregnancy. To understand the implication of these differentially expressed (DE) genes, a systems biology approach was used to construct networks for each organ using three different techniques: Bayesian statistics, sPLS-DA and manual construction of a Process Descriptive (PD) network. The PD network showed DE genes from both organs participating in the same cellular processes that affect mitophagy and neuronal outgrowth. This work may help to evaluate the doctrine for using rKI in case of repetitive or prolonged exposure to radioactive particles upon nuclear accidents.
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