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Mishra A, Tabassum N, Aggarwal A, Kim YM, Khan F. Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces. Antibiotics (Basel) 2024; 13:788. [PMID: 39200087 PMCID: PMC11351874 DOI: 10.3390/antibiotics13080788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.
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Affiliation(s)
- Akanksha Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India;
| | - Nazia Tabassum
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Ashish Aggarwal
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India;
| | - Young-Mog Kim
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Department of Food Science and Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Fazlurrahman Khan
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Institute of Fisheries Science, Pukyong National University, Busan 48513, Republic of Korea
- International Graduate Program of Fisheries Science, Pukyong National University, Busan 48513, Republic of Korea
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Malwe AS, Sharma VK. Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome. Front Microbiol 2023; 14:1254073. [PMID: 38116528 PMCID: PMC10728657 DOI: 10.3389/fmicb.2023.1254073] [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: 07/06/2023] [Accepted: 10/12/2023] [Indexed: 12/21/2023] Open
Abstract
A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.
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Affiliation(s)
| | - Vineet K. Sharma
- MetaBioSys Lab, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
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Debroy R, Ramaiah S. Consolidated knowledge-guided computational pipeline for therapeutic intervention against bacterial biofilms - a review. BIOFOULING 2023; 39:928-947. [PMID: 38108207 DOI: 10.1080/08927014.2023.2294763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Biofilm-associated bacterial infections attributed to multifactorial antimicrobial resistance have caused worldwide challenges in formulating successful treatment strategies. In search of accelerated yet cost-effective therapeutics, several researchers have opted for bioinformatics-based protocols to systemize targeted therapies against biofilm-producing strains. The present review investigated the up-to-date computational databases and servers dedicated to anti-biofilm research to design/screen novel biofilm inhibitors (antimicrobial peptides/phytocompounds/synthetic compounds) and predict their biofilm-inhibition efficacy. Scrutinizing the contemporary in silico methods, a consolidated approach has been highlighted, referred to as a knowledge-guided computational pipeline for biofilm-targeted therapy. The proposed pipeline has amalgamated prominently employed methodologies in genomics, transcriptomics, interactomics and proteomics to identify potential target proteins and their complementary anti-biofilm compounds for effective functional inhibition of biofilm-linked pathways. This review can pave the way for new portals to formulate successful therapeutic interventions against biofilm-producing pathogens.
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Affiliation(s)
- Reetika Debroy
- Medical and Biological Computing Laboratory, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
- Department of Bio-Medical Sciences, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Sudha Ramaiah
- Medical and Biological Computing Laboratory, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
- Department of Bio-Sciences, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
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Liu T, Zhai Y, Jeong KC. Advancing understanding of microbial biofilms through machine learning-powered studies. Food Sci Biotechnol 2023; 32:1653-1664. [PMID: 37780593 PMCID: PMC10533454 DOI: 10.1007/s10068-023-01415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 10/03/2023] Open
Abstract
Microbial biofilms are prevalent in various environments and pose significant challenges to food safety and public health. The biofilms formed by pathogens can cause food spoilage, foodborne illness, and infectious diseases, which are difficult to treat due to their enhanced antimicrobial resistance. While the composition and development of biofilms have been widely studied, their profound impact on food, the food industry, and public health has not been sufficiently recapitulated. This review aims to provide a comprehensive overview of microbial biofilms in the food industry and their implication on public health. It highlights the existence of biofilms along the food-producing chains and the underlying mechanisms of biofilm-associated diseases. Furthermore, this review thoroughly summarizes the enhanced understanding of microbial biofilms achieved through machine learning approaches in biofilm research. By consolidating existing knowledge, this review intends to facilitate developing effective strategies to combat biofilm-associated infections in both the food industry and public health.
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Affiliation(s)
- Ting Liu
- Emerging Pathogens Institute, University of Florida, 2055 Mowry Rd, Gainesville, FL 32610 USA
- Department of Animal Sciences, University of Florida, 2250 Shealy Dr, Gainesville, FL 32608 USA
| | - Yuting Zhai
- Emerging Pathogens Institute, University of Florida, 2055 Mowry Rd, Gainesville, FL 32610 USA
- Department of Animal Sciences, University of Florida, 2250 Shealy Dr, Gainesville, FL 32608 USA
| | - Kwangcheol Casey Jeong
- Emerging Pathogens Institute, University of Florida, 2055 Mowry Rd, Gainesville, FL 32610 USA
- Department of Animal Sciences, University of Florida, 2250 Shealy Dr, Gainesville, FL 32608 USA
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Rajput A, Bhamare KT, Thakur A, Kumar M. Anti-Biofilm: Machine Learning Assisted Prediction of IC 50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing. J Mol Biol 2023; 435:168115. [PMID: 37356913 DOI: 10.1016/j.jmb.2023.168115] [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: 12/01/2022] [Revised: 04/06/2023] [Accepted: 04/14/2023] [Indexed: 06/27/2023]
Abstract
Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC50) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.
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Affiliation(s)
- Akanksha Rajput
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India
| | - Kailash T Bhamare
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Anamika Thakur
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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Raya D, Peta V, Bomgni A, Du Do T, Kalimuthu J, Salem DR, Gadhamshetty V, Gnimpieba EZ, Dhiman SS. Classification of bacterial nanowire proteins using Machine Learning and Feature Engineering model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539336. [PMID: 37205598 PMCID: PMC10187271 DOI: 10.1101/2023.05.03.539336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Nanowires (NW) have been extensively studied for Shewanella spp. and Geobacter spp. and are mostly produced by Type IV pili or multiheme c-type cytochrome. Electron transfer via NW is the most studied mechanism in microbially induced corrosion, with recent interest in application in bioelectronics and biosensor. In this study, a machine learning (ML) based tool was developed to classify NW proteins. A manually curated 999 protein collection was developed as an NW protein dataset. Gene ontology analysis of the dataset revealed microbial NW is part of membranal proteins with metal ion binding motifs and plays a central role in electron transfer activity. Random Forest (RF), support vector machine (SVM), and extreme gradient boost (XGBoost) models were implemented in the prediction model and were observed to identify target proteins based on functional, structural, and physicochemical properties with 89.33%, 95.6%, and 99.99% accuracy. Dipetide amino acid composition, transition, and distribution protein features of NW are key important features aiding in the model's high performance.
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Gupta A, Malwe AS, Srivastava GN, Thoudam P, Hibare K, Sharma VK. MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets. BMC Bioinformatics 2022; 23:507. [PMID: 36443666 PMCID: PMC9703692 DOI: 10.1186/s12859-022-05061-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 11/16/2022] [Indexed: 11/29/2022] Open
Abstract
Bacteria can exceptionally evolve and develop pathogenic features making it crucial to determine novel pathogenic proteins for specific therapeutic interventions. Therefore, we have developed a machine-learning tool that predicts and functionally classifies pathogenic proteins into their respective pathogenic classes. Through construction of pathogenic proteins database and optimization of ML algorithms, Support Vector Machine was selected for the model construction. The developed SVM classifier yielded an accuracy of 81.72% on the blind-dataset and classified the proteins into three classes: Non-pathogenic proteins (Class-1), Antibiotic Resistance Proteins and Toxins (Class-2), and Secretory System Associated and capsular proteins (Class-3). The classifier provided an accuracy of 79% on real dataset-1, and 72% on real dataset-2. Based on the probability of prediction, users can estimate the pathogenicity and annotation of proteins under scrutiny. Tool will provide accurate prediction of pathogenic proteins in genomic and metagenomic datasets providing leads for experimental validations. Tool is available at: http://metagenomics.iiserb.ac.in/mp4 .
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Affiliation(s)
- Ankit Gupta
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
| | - Aditya S. Malwe
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
| | - Gopal N. Srivastava
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
| | - Parikshit Thoudam
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
| | - Keshav Hibare
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
| | - Vineet K. Sharma
- grid.462376.20000 0004 1763 8131MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh India
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Raya D, Shreya A, Kumar A, Giri SK, Salem DR, Gnimpieba EZ, Gadhamshetty V, Dhiman SS. Molecular regulation of conditioning film formation and quorum quenching in sulfate reducing bacteria. Front Microbiol 2022; 13:1008536. [PMID: 36386676 PMCID: PMC9659907 DOI: 10.3389/fmicb.2022.1008536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/26/2022] [Indexed: 09/19/2023] Open
Abstract
Sensing surface topography, an upsurge of signaling biomolecules, and upholding cellular homeostasis are the rate-limiting spatio-temporal events in microbial attachment and biofilm formation. Initially, a set of highly specialized proteins, viz. conditioning protein, directs the irreversible attachment of the microbes. Later signaling molecules, viz. autoinducer, take over the cellular communication phenomenon, resulting in a mature microbial biofilm. The mandatory release of conditioning proteins and autoinducers corroborated the existence of two independent mechanisms operating sequentially for biofilm development. However, both these mechanisms are significantly affected by the availability of the cofactor, e.g., Copper (Cu). Generally, the Cu concentration beyond threshold levels is detrimental to the anaerobes except for a few species of sulfate-reducing bacteria (SRB). Remarkably SRB has developed intricate ways to resist and thrive in the presence of Cu by activating numerous genes responsible for modifying the presence of more toxic Cu(I) to Cu(II) within the periplasm, followed by their export through the outer membrane. Therefore, the determinants of Cu toxicity, sequestration, and transportation are reconnoitered for their contribution towards microbial adaptations and biofilm formation. The mechanistic details revealing Cu as a quorum quencher (QQ) are provided in addition to the three pathways involved in the dissolution of cellular communications. This review articulates the Machine Learning based data curing and data processing for designing novel anti-biofilm peptides and for an in-depth understanding of QQ mechanisms. A pioneering data set has been mined and presented on the functional properties of the QQ homolog in Oleidesulfovibrio alaskensis G20 and residues regulating the multicopper oxidase properties in SRB.
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Affiliation(s)
- Dheeraj Raya
- Department of Civil and Environmental Engineering, South Dakota Mines, Rapid City, SD, United States
- 2DBEST Research Center, South Dakota Mines, Rapid City, SD, United States
| | - Aritree Shreya
- Department of Civil and Environmental Engineering, South Dakota Mines, Rapid City, SD, United States
- 2DBEST Research Center, South Dakota Mines, Rapid City, SD, United States
| | - Anil Kumar
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Shiv Kumar Giri
- Department of Biotechnology, Maharaja Agrasen University, Baddi, Himachal Pradesh, India
| | - David R. Salem
- Chemical and Biological Engineering, South Dakota Mines, Rapid City, SD, United States
| | - Etienne Z. Gnimpieba
- 2DBEST Research Center, South Dakota Mines, Rapid City, SD, United States
- Department of Biomedical Engineering, University of South Dakota, Vermillion, SD, United States
| | - Venkataramana Gadhamshetty
- Department of Civil and Environmental Engineering, South Dakota Mines, Rapid City, SD, United States
- 2DBEST Research Center, South Dakota Mines, Rapid City, SD, United States
| | - Saurabh Sudha Dhiman
- Department of Civil and Environmental Engineering, South Dakota Mines, Rapid City, SD, United States
- 2DBEST Research Center, South Dakota Mines, Rapid City, SD, United States
- Department of Chemistry, Biology and Health Sciences, South Dakota Mines, Rapid City, SD, United States
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Biofilm- i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27154861. [PMID: 35956807 PMCID: PMC9369795 DOI: 10.3390/molecules27154861] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/16/2022] [Accepted: 07/17/2022] [Indexed: 11/19/2022]
Abstract
Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform “Biofilm-i” employing a quantitative structure–activity relationship approach for making generalized predictions, along with group and species-specific predictions of biofilm inhibition efficiency of chemical(s). The platform encompasses eight predictors, three analysis tools, and data visualization modules. The experimentally validated biofilm inhibitors for model development were retrieved from the “aBiofilm” resource and processed using a 10-fold cross-validation approach using the support vector machine and andom forest machine learning techniques. The data was further sub-divided into training/testing and independent validation sets. From training/testing data sets the Pearson’s correlation coefficient of overall chemicals, Gram-positive bacteria, Gram-negative bacteria, fungus, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, and Escherichia coli was 0.60, 0.77, 0.62, 0.77, 0.73, 0.83, 0.70, and 0.71 respectively via Support Vector Machine. Further, all the QSAR models performed equally well on independent validation data sets. Additionally, we also checked the performance of the random forest machine learning technique for the above datasets. The integrated analysis tools can convert the chemical structure into different formats, search for a similar chemical in the aBiofilm database and design the analogs. Moreover, the data visualization modules check the distribution of experimentally validated biofilm inhibitors according to their common scaffolds. The Biofilm-i platform would be of immense help to researchers engaged in designing highly efficacious biofilm inhibitors for tackling the menace of antibiotic drug resistance.
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Application of proper orthogonal decomposition for evaluation of coherent structures and energy contents in microbial biofilms. METHODS IN MICROBIOLOGY 2022; 194:106420. [DOI: 10.1016/j.mimet.2022.106420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 11/17/2022]
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Cruz A, Condinho M, Carvalho B, Arraiano CM, Pobre V, Pinto SN. The Two Weapons against Bacterial Biofilms: Detection and Treatment. Antibiotics (Basel) 2021; 10:1482. [PMID: 34943694 PMCID: PMC8698905 DOI: 10.3390/antibiotics10121482] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/29/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
Bacterial biofilms are defined as complex aggregates of bacteria that grow attached to surfaces or are associated with interfaces. Bacteria within biofilms are embedded in a self-produced extracellular matrix made of polysaccharides, nucleic acids, and proteins. It is recognized that bacterial biofilms are responsible for the majority of microbial infections that occur in the human body, and that biofilm-related infections are extremely difficult to treat. This is related with the fact that microbial cells in biofilms exhibit increased resistance levels to antibiotics in comparison with planktonic (free-floating) cells. In the last years, the introduction into the market of novel compounds that can overcome the resistance to antimicrobial agents associated with biofilm infection has slowed down. If this situation is not altered, millions of lives are at risk, and this will also strongly affect the world economy. As such, research into the identification and eradication of biofilms is important for the future of human health. In this sense, this article provides an overview of techniques developed to detect and imaging biofilms as well as recent strategies that can be applied to treat biofilms during the several biofilm formation steps.
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Affiliation(s)
- Adriana Cruz
- iBB—Institute for Bioengineering and Biosciences, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal;
- i4HB—Institute for Health and Bioeconomy, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Manuel Condinho
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; (M.C.); (B.C.); (C.M.A.)
| | - Beatriz Carvalho
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; (M.C.); (B.C.); (C.M.A.)
| | - Cecília M. Arraiano
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; (M.C.); (B.C.); (C.M.A.)
| | - Vânia Pobre
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal; (M.C.); (B.C.); (C.M.A.)
| | - Sandra N. Pinto
- iBB—Institute for Bioengineering and Biosciences, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal;
- i4HB—Institute for Health and Bioeconomy, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
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12
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An AY, Choi KYG, Baghela AS, Hancock REW. An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents. Front Microbiol 2021; 12:640787. [PMID: 33927701 PMCID: PMC8076610 DOI: 10.3389/fmicb.2021.640787] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/23/2021] [Indexed: 12/29/2022] Open
Abstract
Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.
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Affiliation(s)
| | | | | | - Robert E. W. Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
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Havránková E, Peña-Méndez EM, Csöllei J, Havel J. Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors. Bioorg Chem 2020; 107:104565. [PMID: 33418318 DOI: 10.1016/j.bioorg.2020.104565] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/07/2020] [Accepted: 12/15/2020] [Indexed: 12/21/2022]
Abstract
Simple molecular descriptors of extensive series of 1,3,5-triazinyl sulfonamide derivatives, based on the structure of sulfonamides and their physicochemical properties, were designed and calculated. These descriptors were successfully applied as inputs for artificial neural network (ANN) modelling of the relationship between the structure and biological activity. The optimized ANN architecture was applied to the prediction of the inhibition activity of 1,3,5-triazinyl sulfonamides against human carbonic anhydrase (hCA) II, tumour-associated hCA IX, and their selectivity (hCA II/hCA IX).
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Affiliation(s)
- Eva Havránková
- Masaryk University, Faculty of Pharmacy, Department of Chemical Drugs, Palackého 1-3, CZ-612 42 Brno, Czech Republic
| | - E M Peña-Méndez
- Universidad de La Laguna (ULL), Facultad de Ciencias, Departamento de Química, Unidad Departamental de Química Analítica, 38201 La Laguna, Spain
| | - Jozef Csöllei
- Masaryk University, Faculty of Pharmacy, Department of Chemical Drugs, Palackého 1-3, CZ-612 42 Brno, Czech Republic
| | - Josef Havel
- Masaryk University, Faculty of Science, Department of Chemistry, University Campus, Kamenice 753/5, CZ-625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 656 91 Brno, Czech Republic
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