1
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Wang H, Fan Q, Wang Y, Yi L, Wang Y. Multi-omics analysis reveals genes and metabolites involved in Streptococcus suis biofilm formation. BMC Microbiol 2024; 24:297. [PMID: 39127666 DOI: 10.1186/s12866-024-03448-5] [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: 02/22/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Streptococcus suis is an important zoonotic pathogen. Biofilm formation largely explains the difficulty in preventing and controlling S. suis. However, little is known about the molecular mechanism of S. suis biofilm formation. RESULTS In this study, transcriptomic and metabolomic analyses of S. suis in biofilm and planktonic states were performed to identify key genes and metabolites involved in biofilm formation. A total of 789 differential genes and 365 differential metabolites were identified. By integrating transcriptomics and metabolomics, five main metabolic pathways were identified, including amino acid pathway, nucleotide metabolism pathway, carbon metabolism pathway, vitamin and cofactor metabolism pathway, and aminoacyl-tRNA biosynthesis metabolic pathway. CONCLUSIONS These results provide new insights for exploring the molecular mechanism of S. suis biofilm formation.
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Affiliation(s)
- Haikun Wang
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, 471000, China
- Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China
| | - Qingying Fan
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, 471000, China
- Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China
| | - Yuxin Wang
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, 471000, China
- Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China
| | - Li Yi
- Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China
- College of Life Science, Luoyang Normal University, Luoyang, 471934, China
| | - Yang Wang
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, 471000, China.
- Henan Provincial Engineering Research Center for Detection and Prevention and Control of Emerging Infectious Diseases in Livestock and Poultry, Luoyang, 471003, China.
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2
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de Crécy-Lagard V, Dias R, Friedberg I, Yuan Y, Swairjo MA. Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.01.601547. [PMID: 39005379 PMCID: PMC11244979 DOI: 10.1101/2024.07.01.601547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknownme". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.
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3
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Chao KH, Heinz JM, Hoh C, Mao A, Shumate A, Pertea M, Salzberg SL. Combining DNA and protein alignments to improve genome annotation with LiftOn. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.593026. [PMID: 38798552 PMCID: PMC11118573 DOI: 10.1101/2024.05.16.593026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
As the number and variety of assembled genomes continues to grow, the number of annotated genomes is falling behind, particularly for eukaryotes. DNA-based mapping tools help to address this challenge, but they are only able to transfer annotation between closely-related species. Here we introduce LiftOn, a homology-based software tool that integrates DNA and protein alignments to enhance the accuracy of genome-scale annotation and to allow mapping between relatively distant species. LiftOn's protein-centric algorithm considers both types of alignments, chooses optimal open reading frames, resolves overlapping gene loci, and finds additional gene copies where they exist. LiftOn can reliably transfer annotation between genomes representing members of the same species, as we demonstrate on human, mouse, honey bee, rice, and Arabidopsis thaliana. It can further map annotation effectively across species pairs as far apart as mouse and rat or Drosophila melanogaster and D. erecta.
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Affiliation(s)
- Kuan-Hao Chao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jakob M. Heinz
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Celine Hoh
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alan Mao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alaina Shumate
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mihaela Pertea
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Steven L Salzberg
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21211, USA
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4
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Katsiki AD, Karatzas PE, De Lastic HX, Georgakilas AG, Tsitsilonis O, Vorgias CE. DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments. BIOLOGY 2024; 13:351. [PMID: 38785833 PMCID: PMC11117493 DOI: 10.3390/biology13050351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI's GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance.
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Affiliation(s)
- Anna D. Katsiki
- Department of Biology, School of Science, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Pantelis E. Karatzas
- Unit of Process Control and Informatics, Department of Process Analysis and Plant Design, School of Chemical Engineering, National Technical University of Athens (NTUA), Zografou Campus, 15780 Athens, Greece
| | - Hector-Xavier De Lastic
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou Campus, 15780 Athens, Greece
| | - Alexandros G. Georgakilas
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou Campus, 15780 Athens, Greece
| | - Ourania Tsitsilonis
- Department of Biology, School of Science, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Constantinos E. Vorgias
- Department of Biology, School of Science, National and Kapodistrian University of Athens, 15784 Athens, Greece
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5
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Simoens L, Fijalkowski I, Van Damme P. Exposing the small protein load of bacterial life. FEMS Microbiol Rev 2023; 47:fuad063. [PMID: 38012116 PMCID: PMC10723866 DOI: 10.1093/femsre/fuad063] [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: 05/09/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023] Open
Abstract
The ever-growing repertoire of genomic techniques continues to expand our understanding of the true diversity and richness of prokaryotic genomes. Riboproteogenomics laid the foundation for dynamic studies of previously overlooked genomic elements. Most strikingly, bacterial genomes were revealed to harbor robust repertoires of small open reading frames (sORFs) encoding a diverse and broadly expressed range of small proteins, or sORF-encoded polypeptides (SEPs). In recent years, continuous efforts led to great improvements in the annotation and characterization of such proteins, yet many challenges remain to fully comprehend the pervasive nature of small proteins and their impact on bacterial biology. In this work, we review the recent developments in the dynamic field of bacterial genome reannotation, catalog the important biological roles carried out by small proteins and identify challenges obstructing the way to full understanding of these elusive proteins.
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Affiliation(s)
- Laure Simoens
- iRIP Unit, Laboratory of Microbiology, Department of Biochemistry and Microbiology, Ghent University, K. L. Ledeganckstraat 35, 9000 Ghent, Belgium
| | - Igor Fijalkowski
- iRIP Unit, Laboratory of Microbiology, Department of Biochemistry and Microbiology, Ghent University, K. L. Ledeganckstraat 35, 9000 Ghent, Belgium
| | - Petra Van Damme
- iRIP Unit, Laboratory of Microbiology, Department of Biochemistry and Microbiology, Ghent University, K. L. Ledeganckstraat 35, 9000 Ghent, Belgium
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6
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Bremer E, Calteau A, Danchin A, Harwood C, Helmann JD, Médigue C, Palsson BO, Sekowska A, Vallenet D, Zuniga A, Zuniga C. A model industrial workhorse:
Bacillus subtilis
strain 168 and its genome after a quarter of a century. Microb Biotechnol 2023; 16:1203-1231. [PMID: 37002859 DOI: 10.1111/1751-7915.14257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).
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Affiliation(s)
- Erhard Bremer
- Department of Biology, Laboratory for Microbiology and Center for Synthetic Microbiology (SYNMIKRO) Philipps‐University Marburg Marburg Germany
| | - Alexandra Calteau
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Antoine Danchin
- School of Biomedical Sciences, Li KaShing Faculty of Medicine Hong Kong University Pokfulam SAR Hong Kong China
| | - Colin Harwood
- Centre for Bacterial Cell Biology, Biosciences Institute Newcastle University Baddiley Clark Building Newcastle upon Tyne UK
| | - John D. Helmann
- Department of Microbiology Cornell University Ithaca New York USA
| | - Claudine Médigue
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Bernhard O. Palsson
- Department of Bioengineering University of California San Diego La Jolla USA
| | | | - David Vallenet
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Abril Zuniga
- Department of Biology San Diego State University San Diego California USA
| | - Cristal Zuniga
- Bioinformatics and Medical Informatics Graduate Program San Diego State University San Diego California USA
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7
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Zheng Y, Young ND, Song J, Chang BC, Gasser RB. An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus. Comput Struct Biotechnol J 2023; 21:2696-2704. [PMID: 37143762 PMCID: PMC10151223 DOI: 10.1016/j.csbj.2023.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.
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8
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Byrne JA, Park Y, Richardson RAK, Pathmendra P, Sun M, Stoeger T. Protection of the human gene research literature from contract cheating organizations known as research paper mills. Nucleic Acids Res 2022; 50:12058-12070. [PMID: 36477580 PMCID: PMC9757046 DOI: 10.1093/nar/gkac1139] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Human gene research generates new biology insights with translational potential, yet few studies have considered the health of the human gene literature. The accessibility of human genes for targeted research, combined with unreasonable publication pressures and recent developments in scholarly publishing, may have created a market for low-quality or fraudulent human gene research articles, including articles produced by contract cheating organizations known as paper mills. This review summarises the evidence that paper mills contribute to the human gene research literature at scale and outlines why targeted gene research may be particularly vulnerable to systematic research fraud. To raise awareness of targeted gene research from paper mills, we highlight features of problematic manuscripts and publications that can be detected by gene researchers and/or journal staff. As improved awareness and detection could drive the further evolution of paper mill-supported publications, we also propose changes to academic publishing to more effectively deter and correct problematic publications at scale. In summary, the threat of paper mill-supported gene research highlights the need for all researchers to approach the literature with a more critical mindset, and demand publications that are underpinned by plausible research justifications, rigorous experiments and fully transparent reporting.
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Affiliation(s)
- Jennifer A Byrne
- To whom correspondence should be addressed. Tel: +61 2 4920 4135;
| | - Yasunori Park
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
| | - Reese A K Richardson
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, USA
| | - Pranujan Pathmendra
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
| | - Mengyi Sun
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, USA
| | - Thomas Stoeger
- To whom correspondence should be addressed. Tel: +61 2 4920 4135;
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9
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Maia GA, Filho VB, Kawagoe EK, Teixeira Soratto TA, Moreira RS, Grisard EC, Wagner G. AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data. Front Genet 2022; 13:1020100. [DOI: 10.3389/fgene.2022.1020100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.
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10
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Danchin A. Pasteur and "motivated" research. C R Biol 2022; 345:109-119. [PMID: 36852600 DOI: 10.5802/crbiol.89] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 11/07/2022]
Abstract
Pasteur's originality in the way he developed pure research is to have understood the importance, for society, of the underlying motivation. Curiosity, of course, is a strong motivation, which explains why we seek to understand the origin of life. But, in front of the immensity of the possible choices, why not, also, choose to start from questions of economic interest (diseases of beer and wine, diseases affecting the silk industry ...) Finally, of course, health is a constant preoccupation, but the diseases, which have no borders, often come from tropical countries and Asia especially. It is therefore necessary to settle there, but not to come and impose one's point of view, but on the contrary to use the knowledge coming from the local culture in order to open new ways of understanding the reality of the world.
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11
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Goudey B, Geard N, Verspoor K, Zobel J. Propagation, detection and correction of errors using the sequence database network. Brief Bioinform 2022; 23:6764545. [PMID: 36266246 PMCID: PMC9677457 DOI: 10.1093/bib/bbac416] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/31/2022] [Accepted: 08/28/2022] [Indexed: 12/14/2022] Open
Abstract
Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.
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Affiliation(s)
- Benjamin Goudey
- Corresponding author. Benjamin Goudey, School of Computing and Information Systems, University of Melbourne Parkville, Victoria, 3010,
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne Parkville, Victoria, 3010
| | - Karin Verspoor
- School of Computing Technologies, RMIT University Melbourne, Victoria, 3000
| | - Justin Zobel
- School of Computing and Information Systems, University of Melbourne Parkville, Victoria, 3010
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12
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Westhof E. Data, data, burning deep, in the forests of the net. Biochem Biophys Res Commun 2022; 633:42-44. [DOI: 10.1016/j.bbrc.2022.09.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 11/28/2022]
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13
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Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism. BIOLOGY 2022; 11:biology11081208. [PMID: 36009835 PMCID: PMC9404739 DOI: 10.3390/biology11081208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/03/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022]
Abstract
Simple Summary The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components. Abstract In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.
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14
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Escudeiro P, Henry CS, Dias RP. Functional characterization of prokaryotic dark matter: the road so far and what lies ahead. CURRENT RESEARCH IN MICROBIAL SCIENCES 2022; 3:100159. [PMID: 36561390 PMCID: PMC9764257 DOI: 10.1016/j.crmicr.2022.100159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/18/2022] [Accepted: 08/05/2022] [Indexed: 12/25/2022] Open
Abstract
Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.
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Affiliation(s)
- Pedro Escudeiro
- BioISI - Instituto de Biosistemas e Ciências Integrativas, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal
| | - Christopher S. Henry
- Argonne National Laboratory, Lemont, Illinois, USA,University of Chicago, Chicago, Illinois, USA
| | - Ricardo P.M. Dias
- BioISI - Instituto de Biosistemas e Ciências Integrativas, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal,iXLab - Innovation for National Biological Resilience, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal,Corresponding author.
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15
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Wang Y, Dong Q, Hu S, Zou H, Wu T, Shi J, Zhang H, Sheng Y, Sun W, Kong X, Chen L. Decoding microbial genomes to understand their functional roles in human complex diseases. IMETA 2022; 1:e14. [PMID: 38868571 PMCID: PMC10989872 DOI: 10.1002/imt2.14] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 01/20/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2024]
Abstract
Complex diseases such as cardiovascular disease (CVD), obesity, inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D), and cancer have become a major burden to public health and affect more than 20% of the population worldwide. The etiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g., dietary habits), and by their interactions. Besides this, increasing pieces of evidence now highlight that the intestinal microbiota may contribute substantially to the health and disease of the human host via their metabolic molecules. Therefore, decoding the microbial genomes has been an important strategy to shed light on their functional potential. In this review, we summarize the roles of the gut microbiome in complex diseases from its functional perspective. We further introduce artificial tools in decoding microbial genomes to profile their functionalities. Finally, state-of-the-art techniques have been highlighted which may contribute to a mechanistic understanding of the gut microbiome in human complex diseases and promote the development of the gut microbiome-based personalized medicine.
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Affiliation(s)
- Yifeng Wang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Quanbin Dong
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Shixian Hu
- Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat‐Sen UniversitySun Yat‐Sen UniversityGuangzhouGuangdongChina
| | - Huayiyang Zou
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
| | - Tingting Wu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
| | - Jing Shi
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
| | - Haifeng Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Yanhui Sheng
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Wei Sun
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Xiangqing Kong
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
| | - Lianmin Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjing Medical UniversityNanjingJiangsuChina
- Cardiovascular Research Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu SchoolNanjing Medical UniversitySuzhouJiangsuChina
- Department of Genetics, University Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
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16
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Park Y, West RA, Pathmendra P, Favier B, Stoeger T, Capes-Davis A, Cabanac G, Labbé C, Byrne JA. Identification of human gene research articles with wrongly identified nucleotide sequences. Life Sci Alliance 2022; 5:e202101203. [PMID: 35022248 PMCID: PMC8807875 DOI: 10.26508/lsa.202101203] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 01/01/2023] Open
Abstract
Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.
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Affiliation(s)
- Yasunori Park
- Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Rachael A West
- Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
- Children's Cancer Research Unit, Kids Research, The Children's Hospital at Westmead, Westmead, Australia
| | | | - Bertrand Favier
- Université Grenoble Alpes, Translationnelle et Innovation en Médecine et Complexité, Grenoble, France
| | - Thomas Stoeger
- Successful Clinical Response in Pneumonia Therapy Systems Biology Center, Northwestern University, Evanston, IL, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Center for Genetic Medicine, Northwestern University School of Medicine, Chicago, IL, USA
| | - Amanda Capes-Davis
- Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
- CellBank Australia, Children's Medical Research Institute, Westmead, Australia
| | - Guillaume Cabanac
- Computer Science Department, Institut de Recherche en Informatique de Toulouse, Unité Mixte de Recherche 5505 Centre National de la Recherche Scientifique (CNRS), University of Toulouse, Toulouse, France
| | - Cyril Labbé
- Université Grenoble Alpes, CNRS, Grenoble INP, Laboratoire d'Informatique de Grenoble, Grenoble, France
| | - Jennifer A Byrne
- Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
- New South Wales Health Statewide Biobank, New South Wales Health Pathology, Camperdown, Australia
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17
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Danchin A. In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology. Microb Biotechnol 2022; 15:42-64. [PMID: 34570957 PMCID: PMC8719824 DOI: 10.1111/1751-7915.13937] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022] Open
Abstract
Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.
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Affiliation(s)
- Antoine Danchin
- Kodikos LabsInstitut Cochin24 rue du Faubourg Saint‐JacquesParis75014France
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18
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Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class. PLoS Comput Biol 2021; 17:e1009446. [PMID: 34555022 PMCID: PMC8491902 DOI: 10.1371/journal.pcbi.1009446] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/05/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation. Correct annotation of genomes is crucial for our understanding and utilization of functional gene diversity, yet the reliability of current protein annotations in public databases is largely unknown. In our work we validated annotations to an S-2-hydroxyacid oxidase enzyme class (EC 1.1.3.15) by assessing activity of 122 representative sequences in a high-throughput screening experiment. From this dataset we inferred that at least 78% of the sequences in the enzyme class are misannotated, and confirmed four alternative activities among the misannotated sequences. We showed that the misannotation is widespread throughout enzyme classes, affecting even well-studied classes of industrial relevance. Overall, our study highlights the value of experimental and computational validation of predicted functions within individual enzyme classes.
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19
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Buzdin AV, Patrushev MV, Sverdlov ED. Will Plant Genome Editing Play a Decisive Role in "Quantum-Leap" Improvements in Crop Yield to Feed an Increasing Global Human Population? PLANTS (BASEL, SWITZERLAND) 2021; 10:1667. [PMID: 34451712 PMCID: PMC8398637 DOI: 10.3390/plants10081667] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/04/2021] [Accepted: 08/07/2021] [Indexed: 02/08/2023]
Abstract
Growing scientific evidence demonstrates unprecedented planetary-scale human impacts on the Earth's system with a predicted threat to the existence of the terrestrial biosphere due to population increase, resource depletion, and pollution. Food systems account for 21-34% of global carbon dioxide (CO2) emissions. Over the past half-century, water and land-use changes have significantly impacted ecosystems, biogeochemical cycles, biodiversity, and climate. At the same time, food production is falling behind consumption, and global grain reserves are shrinking. Some predictions suggest that crop yields must approximately double by 2050 to adequately feed an increasing global population without a large expansion of crop area. To achieve this, "quantum-leap" improvements in crop cultivar productivity are needed within very narrow planetary boundaries of permissible environmental perturbations. Strategies for such a "quantum-leap" include mutation breeding and genetic engineering of known crop genome sequences. Synthetic biology makes it possible to synthesize DNA fragments of any desired sequence, and modern bioinformatics tools may hopefully provide an efficient way to identify targets for directed modification of selected genes responsible for known important agronomic traits. CRISPR/Cas9 is a new technology for incorporating seamless directed modifications into genomes; it is being widely investigated for its potential to enhance the efficiency of crop production. We consider the optimism associated with the new genetic technologies in terms of the complexity of most agronomic traits, especially crop yield potential (Yp) limits. We also discuss the possible directions of overcoming these limits and alternative ways of providing humanity with food without transgressing planetary boundaries. In conclusion, we support the long-debated idea that new technologies are unlikely to provide a rapidly growing population with significantly increased crop yield. Instead, we suggest that delicately balanced humane measures to limit its growth and the amount of food consumed per capita are highly desirable for the foreseeable future.
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Affiliation(s)
- Anton V Buzdin
- The Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701 Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Maxim V Patrushev
- Kurchatov Center for Genome Research, National Research Center Kurchatov Institute, 123182 Moscow, Russia
| | - Eugene D Sverdlov
- Kurchatov Center for Genome Research, National Research Center Kurchatov Institute, 123182 Moscow, Russia
- Institute of Molecular Genetics, National Research Center Kurchatov Institute, 123182 Moscow, Russia
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20
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Souvorov A, Agarwala R. SAUTE: sequence assembly using target enrichment. BMC Bioinformatics 2021; 22:375. [PMID: 34289805 PMCID: PMC8293564 DOI: 10.1186/s12859-021-04174-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/05/2021] [Indexed: 01/25/2023] Open
Abstract
Background Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multiple well supported variants for assembled regions. Results To facilitate assembly of repeat regions and to report multiple well supported variants when a user can provide target sequences to assist the assembly, we propose SAUTE and SAUTE_PROT assemblers. Both assemblers use de Bruijn graph on reads. Targets can be transcripts or proteins for RNA-seq reads and transcripts, proteins, or genomic regions for genomic reads. Target sequences are nucleotide and protein sequences for SAUTE and SAUTE_PROT, respectively. Conclusions For RNA-seq, comparisons with Trinity, rnaSPAdes, SPAligner, and SPAdes assembly of reads aligned to target proteins by DIAMOND show that SAUTE_PROT finds more coding sequences that translate to benchmark proteins. Using AMRFinderPlus calls, we find SAUTE has higher sensitivity and precision than SPAdes, plasmidSPAdes, SPAligner, and SPAdes assembly of reads aligned to target regions by HISAT2. It also has better sensitivity than SKESA but worse precision. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04174-9.
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Affiliation(s)
| | - Richa Agarwala
- NCBI/NLM/NIH/DHHS, 8600 Rockville Pike, Bethesda, MD, 20894, USA.
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21
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Delwiche CF. Microbial biodiversity: A newly isolated cyanobacterium sheds light on the evolution of photosynthesis. Curr Biol 2021; 31:R843-R845. [PMID: 34256915 DOI: 10.1016/j.cub.2021.05.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
A newly isolated cyanobacterium found growing in close association with a tropical, non-vascular plant has been cultured and its genome sequenced. Its lineage is well over a billion years old and gives insights into the evolutionary origin of oxygenic photosynthesis.
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Affiliation(s)
- Charles F Delwiche
- Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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22
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Open Issues for Protein Function Assignment in Haloferax volcanii and Other Halophilic Archaea. Genes (Basel) 2021; 12:genes12070963. [PMID: 34202810 PMCID: PMC8305020 DOI: 10.3390/genes12070963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Annotation ambiguities and annotation errors are a general challenge in genomics. While a reliable protein function assignment can be obtained by experimental characterization, this is expensive and time-consuming, and the number of such Gold Standard Proteins (GSP) with experimental support remains very low compared to proteins annotated by sequence homology, usually through automated pipelines. Even a GSP may give a misleading assignment when used as a reference: the homolog may be close enough to support isofunctionality, but the substrate of the GSP is absent from the species being annotated. In such cases, the enzymes cannot be isofunctional. Here, we examined a variety of such issues in halophilic archaea (class Halobacteria), with a strong focus on the model haloarchaeon Haloferax volcanii. Results: Annotated proteins of Hfx. volcanii were identified for which public databases tend to assign a function that is probably incorrect. In some cases, an alternative, probably correct, function can be predicted or inferred from the available evidence, but this has not been adopted by public databases because experimental validation is lacking. In other cases, a probably invalid specific function is predicted by homology, and while there is evidence that this assigned function is unlikely, the true function remains elusive. We listed 50 of those cases, each with detailed background information, so that a conclusion about the most likely biological function can be drawn. For reasons of brevity and comprehension, only the key aspects are listed in the main text, with detailed information being provided in a corresponding section of the Supplementary Materials. Conclusions: Compiling, describing and summarizing these open annotation issues and functional predictions will benefit the scientific community in the general effort to improve the evaluation of protein function assignments and more thoroughly detail them. By highlighting the gaps and likely annotation errors currently in the databases, we hope this study will provide a framework for experimentalists to systematically confirm (or disprove) our function predictions or to uncover yet more unexpected functions.
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23
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Liu Z, Ma A, Mathé E, Merling M, Ma Q, Liu B. Network analyses in microbiome based on high-throughput multi-omics data. Brief Bioinform 2021; 22:1639-1655. [PMID: 32047891 PMCID: PMC7986608 DOI: 10.1093/bib/bbaa005] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 02/06/2023] Open
Abstract
Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.
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Affiliation(s)
- Zhaoqian Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Marlena Merling
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
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24
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Koonin EV, Makarova KS, Wolf YI. Evolution of Microbial Genomics: Conceptual Shifts over a Quarter Century. Trends Microbiol 2021; 29:582-592. [PMID: 33541841 DOI: 10.1016/j.tim.2021.01.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 12/20/2022]
Abstract
Prokaryote genomics started in earnest in 1995, with the complete sequences of two small bacterial genomes, those of Haemophilus influenzae and Mycoplasma genitalium. During the next quarter century, the prokaryote genome database has been growing exponentially, with no saturation in sight. For most of these 25 years, genome sequencing remained limited to cultivable microbes. Together with next-generation sequencing methods, advances in metagenomics and single-cell genomics have lifted this limitation, providing for an increasingly unbiased characterization of the global prokaryote diversity. Advances in computational genomics followed the progress of genome sequencing, even if occasionally lagging behind. Several major new branches of bacteria and archaea were discovered, including Asgard archaea, the apparent closest relatives of eukaryotes and expansive groups of bacteria and archaea with small genomes thought to be symbionts of other prokaryotes. Comparative analysis of numerous prokaryote genomes spanning a wide range of evolutionary distances changed the conceptual foundations of microbiology, supplanting the notion of species genomes with fixed gene sets with that of dynamic pangenomes and the notion of a single Tree of Life (ToL) with a statistical tree-like trend among individual gene trees. Strides were also made towards a theory and quantitative laws of prokaryote genome evolution.
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Affiliation(s)
- Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA.
| | - Kira S Makarova
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA
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25
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Bioinformatics Tools for Gene and Genome Annotation Analysis of Microbes for Synthetic Biology and Cancer Biology Applications. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_16] [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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Grasso S, van Rij T, van Dijl JM. GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting. Brief Bioinform 2020; 22:5998864. [PMID: 33227814 PMCID: PMC8294519 DOI: 10.1093/bib/bbaa302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 11/17/2022] Open
Abstract
Subcellular localization is a critical aspect of protein function and the potential application of proteins either as drugs or drug targets, or in industrial and domestic applications. However, the experimental determination of protein localization is time consuming and expensive. Therefore, various localization predictors have been developed for particular groups of species. Intriguingly, despite their major representation amongst biotechnological cell factories and pathogens, a meta-predictor based on sorting signals and specific for Gram-positive bacteria was still lacking. Here we present GP4, a protein subcellular localization meta-predictor mainly for Firmicutes, but also Actinobacteria, based on the combination of multiple tools, each specific for different sorting signals and compartments. Novelty elements include improved cell-wall protein prediction, including differentiation of the type of interaction, prediction of non-canonical secretion pathway target proteins, separate prediction of lipoproteins and better user experience in terms of parsability and interpretability of the results. GP4 aims at mimicking protein sorting as it would happen in a bacterial cell. As GP4 is not homology based, it has a broad applicability and does not depend on annotated databases with homologous proteins. Non-canonical usage may include little studied or novel species, synthetic and engineered organisms, and even re-use of the prediction data to develop custom prediction algorithms. Our benchmark analysis highlights the improved performance of GP4 compared to other widely used subcellular protein localization predictors. A webserver running GP4 is available at http://gp4.hpc.rug.nl/
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Affiliation(s)
| | | | - Jan Maarten van Dijl
- University of Groningen and the University Medical Center Groningen, the Netherlands
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27
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Meyer C, Scalzitti N, Jeannin-Girardon A, Collet P, Poch O, Thompson JD. Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes. BMC Bioinformatics 2020; 21:513. [PMID: 33172385 PMCID: PMC7656754 DOI: 10.1186/s12859-020-03855-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/30/2020] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will significantly affect subsequent analyses. Results We first investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence. We then focused on the mismatched sequence errors that cause particular problems for downstream applications. A detailed characterization allowed us to identify the potential causes for the gene misprediction in approximately half (5446) of these cases. As a proof-of-concept, we also developed a simple method which allowed us to propose improved sequences for 603 primate proteins. Conclusions Gene prediction errors in primate proteomes affect up to 50% of the sequences. Major causes of errors include undetermined genome regions, genome sequencing or assembly issues, and limitations in the models used to represent gene exon–intron structures. Nevertheless, existing genome sequences can still be exploited to improve protein sequence quality. Perspectives of the work include the characterization of other types of gene prediction errors, as well as the development of a more comprehensive algorithm for protein sequence error correction.
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Affiliation(s)
- Corentin Meyer
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Nicolas Scalzitti
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Anne Jeannin-Girardon
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Pierre Collet
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Olivier Poch
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Julie D Thompson
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
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28
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Ejigu GF, Jung J. Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing. BIOLOGY 2020; 9:E295. [PMID: 32962098 PMCID: PMC7565776 DOI: 10.3390/biology9090295] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/13/2020] [Accepted: 09/16/2020] [Indexed: 12/16/2022]
Abstract
Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.
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Affiliation(s)
| | - Jaehee Jung
- Department of Information and Communication Engineering, Myongji University, Yongin-si 17058, Gyeonggi-do, Korea;
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29
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Scalzitti N, Jeannin-Girardon A, Collet P, Poch O, Thompson JD. A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms. BMC Genomics 2020; 21:293. [PMID: 32272892 PMCID: PMC7147072 DOI: 10.1186/s12864-020-6707-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/30/2020] [Indexed: 02/02/2023] Open
Abstract
Background The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations. Results We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools. Conclusions The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.
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Affiliation(s)
- Nicolas Scalzitti
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Anne Jeannin-Girardon
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Pierre Collet
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Olivier Poch
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Julie D Thompson
- Department of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
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30
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Breitwieser FP, Pertea M, Zimin AV, Salzberg SL. Human contamination in bacterial genomes has created thousands of spurious proteins. Genome Res 2019; 29:954-960. [PMID: 31064768 PMCID: PMC6581058 DOI: 10.1101/gr.245373.118] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 05/03/2019] [Indexed: 01/22/2023]
Abstract
Contaminant sequences that appear in published genomes can cause numerous problems for downstream analyses, particularly for evolutionary studies and metagenomics projects. Our large-scale scan of complete and draft bacterial and archaeal genomes in the NCBI RefSeq database reveals that 2250 genomes are contaminated by human sequence. The contaminant sequences derive primarily from high-copy human repeat regions, which themselves are not adequately represented in the current human reference genome, GRCh38. The absence of the sequences from the human assembly offers a likely explanation for their presence in bacterial assemblies. In some cases, the contaminating contigs have been erroneously annotated as containing protein-coding sequences, which over time have propagated to create spurious protein “families” across multiple prokaryotic and eukaryotic genomes. As a result, 3437 spurious protein entries are currently present in the widely used nr and TrEMBL protein databases. We report here an extensive list of contaminant sequences in bacterial genome assemblies and the proteins associated with them. We found that nearly all contaminants occurred in small contigs in draft genomes, which suggests that filtering out small contigs from draft genome assemblies may mitigate the issue of contamination while still keeping nearly all of the genuine genomic sequences.
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Affiliation(s)
- Florian P Breitwieser
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA
| | - Mihaela Pertea
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA.,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Aleksey V Zimin
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Steven L Salzberg
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA.,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.,Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA
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31
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Hallberg ZF, Chan CH, Wright TA, Kranzusch PJ, Doxzen KW, Park JJ, Bond DR, Hammond MC. Structure and mechanism of a Hypr GGDEF enzyme that activates cGAMP signaling to control extracellular metal respiration. eLife 2019; 8:43959. [PMID: 30964001 PMCID: PMC6456294 DOI: 10.7554/elife.43959] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 03/12/2019] [Indexed: 12/16/2022] Open
Abstract
A newfound signaling pathway employs a GGDEF enzyme with unique activity compared to the majority of homologs associated with bacterial cyclic di-GMP signaling. This system provides a rare opportunity to study how signaling proteins natively gain distinct function. Using genetic knockouts, riboswitch reporters, and RNA-Seq, we show that GacA, the Hypr GGDEF in Geobacter sulfurreducens, specifically regulates cyclic GMP-AMP (3′,3′-cGAMP) levels in vivo to stimulate gene expression associated with metal reduction separate from electricity production. To reconcile these in vivo findings with prior in vitro results that showed GacA was promiscuous, we developed a full kinetic model combining experimental data and mathematical modeling to reveal mechanisms that contribute to in vivo specificity. A 1.4 Å-resolution crystal structure of the Geobacter Hypr GGDEF domain was determined to understand the molecular basis for those mechanisms, including key cross-dimer interactions. Together these results demonstrate that specific signaling can result from a promiscuous enzyme. Microscopic organisms known as bacteria are found in virtually every environment on the planet. One reason bacteria are so successful is that they are able to form communities known as biofilms on surfaces in animals and other living things, as well as on rocks and other features in the environment. These biofilms protect the bacteria from fluctuations in the environment and toxins. For over 30 years, a class of enzymes called the GGDEF enzymes were thought to make a single signal known as cyclic di-GMP that regulates the formation of biofilms. However, in 2016, a team of researchers reported that some GGDEF enzymes, including one from a bacterium called Geobacter sulfurreducens, were also able to produce two other signals known as cGAMP and cyclic di-AMP. The experiments involved making the enzymes and testing their activity outside the cell. Therefore, it remained unclear whether these enzymes (dubbed ‘Hypr’ GGDEF enzymes) actually produce all three signals inside cells and play a role in forming bacterial biofilms. G. sulfurreducens is unusual because it is able to grow on metallic minerals or electrodes to generate electrical energy. As part of a community of microorganisms, they help break down pollutants in contaminated areas and can generate electricity from wastewater. Now, Hallberg, Chan et al. – including many of the researchers involved in the 2016 work – combined several experimental and mathematical approaches to study the Hypr GGDEF enzymes in G. sulfurreducens. The experiments show that the Hypr GGDEF enzymes produced cGAMP, but not the other two signals, inside the cells. This cGAMP regulated the ability of G. sulfurreducens to grow by extracting electrical energy from the metallic minerals, which appears to be a new, biofilm-less lifestyle. Further experiments revealed how Hypr GGDEF enzymes have evolved to preferentially make cGAMP over the other two signals. Together, these findings demonstrate that enzymes with the ability to make several different signals, are capable of generating specific responses in bacterial cells. By understanding how bacteria make decisions, it may be possible to change their behaviors. The findings of Hallberg, Chan et al. help to identify the signaling pathways involved in this decision-making and provide new tools to study them in the future.
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Affiliation(s)
- Zachary F Hallberg
- Department of Chemistry, University of California, Berkeley, Berkeley, United States
| | - Chi Ho Chan
- Department of Plant and Microbial Biology and BioTechnology Institute, University of Minnesota, Minnesota, United States
| | - Todd A Wright
- Department of Chemistry, University of California, Berkeley, Berkeley, United States
| | - Philip J Kranzusch
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, United States.,Parker Institute for Cancer Immunotherapy at Dana-Farber Cancer Institute, Boston, United States
| | - Kevin W Doxzen
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
| | - James J Park
- Department of Chemistry, University of California, Berkeley, Berkeley, United States
| | - Daniel R Bond
- Department of Plant and Microbial Biology and BioTechnology Institute, University of Minnesota, Minnesota, United States
| | - Ming C Hammond
- Department of Chemistry, University of California, Berkeley, Berkeley, United States.,Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, United States.,Department of Chemistry, University of Utah, Salt Lake City, United States
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32
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Abstract
In this issue of JEM, Reinink et al. (https://doi.org/10.1084/jem.20181812) use comparative lipidomics to identify a new family of trehalose-containing cell wall lipids that are enriched in virulent Salmonella serovars. These lipids are structurally related to the important mycobacterial immunogen cord factor.
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Affiliation(s)
- Stephanie R Lovell-Read
- Mycobacterial Metabolism and Antibiotic Research Laboratory, The Francis Crick Institute, London, UK
- Department of Chemistry, University of Oxford, Oxford, UK
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33
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Boël G, Danot O, de Lorenzo V, Danchin A. Omnipresent Maxwell's demons orchestrate information management in living cells. Microb Biotechnol 2019; 12:210-242. [PMID: 30806035 PMCID: PMC6389857 DOI: 10.1111/1751-7915.13378] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The development of synthetic biology calls for accurate understanding of the critical functions that allow construction and operation of a living cell. Besides coding for ubiquitous structures, minimal genomes encode a wealth of functions that dissipate energy in an unanticipated way. Analysis of these functions shows that they are meant to manage information under conditions when discrimination of substrates in a noisy background is preferred over a simple recognition process. We show here that many of these functions, including transporters and the ribosome construction machinery, behave as would behave a material implementation of the information-managing agent theorized by Maxwell almost 150 years ago and commonly known as Maxwell's demon (MxD). A core gene set encoding these functions belongs to the minimal genome required to allow the construction of an autonomous cell. These MxDs allow the cell to perform computations in an energy-efficient way that is vastly better than our contemporary computers.
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Affiliation(s)
- Grégory Boël
- UMR 8261 CNRS‐University Paris DiderotInstitut de Biologie Physico‐Chimique13 rue Pierre et Marie Curie75005ParisFrance
| | - Olivier Danot
- Institut Pasteur25‐28 rue du Docteur Roux75724Paris Cedex 15France
| | - Victor de Lorenzo
- Molecular Environmental Microbiology LaboratorySystems Biology ProgrammeCentro Nacional de BiotecnologiaC/Darwin n° 3, Campus de Cantoblanco28049MadridEspaña
| | - Antoine Danchin
- Institute of Cardiometabolism and NutritionHôpital de la Pitié‐Salpêtrière47 Boulevard de l'Hôpital75013ParisFrance
- The School of Biomedical SciencesLi Kashing Faculty of MedicineHong Kong University21, Sassoon RoadPokfulamSAR Hong Kong
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34
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Danchin A. Conceptual sequel to biological expeditions at the time of global changes. ENVIRONMENTAL MICROBIOLOGY REPORTS 2019; 11:38-40. [PMID: 30516032 DOI: 10.1111/1758-2229.12722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 11/14/2018] [Indexed: 06/09/2023]
Affiliation(s)
- Antoine Danchin
- Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, Paris, France
- School of Biomedical Sciences, Li Kashing Faculty of Medicine, Hong Kong University, Pokfulam, SAR, Hong Kong
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35
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Huang WE, Nikel PI. The Synthetic Microbiology Caucus: from abstract ideas to turning microbes into cellular machines and back. Microb Biotechnol 2019; 12:5-7. [PMID: 30461208 PMCID: PMC6302714 DOI: 10.1111/1751-7915.13337] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
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36
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Ouzounis CA. Developing computational biology at meridian 23° E, and a little eastwards. ACTA ACUST UNITED AC 2018; 25:18. [PMID: 30460210 PMCID: PMC6237004 DOI: 10.1186/s40709-018-0091-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 11/09/2018] [Indexed: 11/23/2022]
Abstract
Modern biology is experiencing a deep transformation by the expansion of molecular-level measurements at all scales, using omics technologies. A key element in this transformation is the field of bioinformatics, that has—in the meanwhile—permeated pretty much all of biological and biomedical research and is now emerging as a key inter-disciplinary area that connects the natural sciences, chemical and electrical engineering, science education and science policy, on a number of science and technology fronts. The strong tradition of open access for large volumes of raw data, collections of complex results and high-quality algorithm implementations in bioinformatics makes the field a unique, special case of open science. We report on our recent research activities, the development of training initiatives in the wider region during the past years, and the lessons learned regarding our efforts away from major epicenters, within the general context of open science.
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Affiliation(s)
- Christos A Ouzounis
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), PO Box 361, 57001 Thessaloníki, Greece
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37
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Shi Y, Pan C, Cen S, Fu L, Cao X, Wang H, Wang K, Wu B. Comparative metabolomics reveals defence-related modification of citrinin by Penicillium citrinum within a synthetic Penicillium-Pseudomonas community. Environ Microbiol 2018; 21:496-510. [PMID: 30452116 DOI: 10.1111/1462-2920.14482] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 11/13/2018] [Indexed: 12/11/2022]
Abstract
Co-occurring microorganisms have been proved to influence the performance of each other by metabolic means in nature. Here we generated a synthetic fungal-bacterial community comprising Penicillium citrinum and Pseudomonas aeruginosa employing the previously described membrane-separated co-culture device. By applying a newly designed molecular networking routine, new citrinin-related metabolites induced by the fungal-bacterial cross-talk were unveiled in trace amounts. A mechanically cycled co-culture setup with external pumping forces accelerating the chemically interspecies communication was then developed to boost the production of cross-talk-induced metabolites. Multivariate data analysis combined with molecular networking revealed the accumulation of a pair of co-culture-induced molecules whose productions were positively correlated to the exchange rate in the new co-cultures, facilitating the discovery of the previously undescribed antibiotic citrinolide with a novel skeleton. This highly oxidized citrinin adduct showed significantly enhanced antibiotic property against the partner strain P. aeruginosa than its precursor citrinin, suggesting a role in the microbial competition. Thus, we propose competitive-advantage-oriented structural modification driven by microbial defence response mechanism in the interspecies cross-talk might be a promising approach in the search for novel antibiotics. Besides, this study highlights the utility of MS-based metabolomics as an effective tool in the direct biochemical analysis of the community metabolism.
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Affiliation(s)
- Yutong Shi
- Ocean College, Zhejiang University, Hangzhou 310058, China
| | - Chengqian Pan
- Ocean College, Zhejiang University, Hangzhou 310058, China
| | - Suoyu Cen
- Ocean College, Zhejiang University, Hangzhou 310058, China
| | - Leilei Fu
- Ocean College, Zhejiang University, Hangzhou 310058, China
| | - Xun Cao
- Ocean College, Zhejiang University, Hangzhou 310058, China
| | - Hong Wang
- School of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Kuiwu Wang
- Department of Applied Chemistry, Zhejiang Gongshang University, Hangzhou 310058, China
| | - Bin Wu
- Ocean College, Zhejiang University, Hangzhou 310058, China
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38
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Danchin A, Ouzounis C, Tokuyasu T, Zucker JD. No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects. Microb Biotechnol 2018; 11:588-605. [PMID: 29806194 PMCID: PMC6011933 DOI: 10.1111/1751-7915.13284] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.
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Affiliation(s)
- Antoine Danchin
- Integromics, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, 47 Boulevard de l'Hôpital, 75013, Paris, France
- School of Biomedical Sciences, Li KaShing Faculty of Medicine, Hong Kong University, 21 Sassoon Road, Pokfulam, Hong Kong
| | - Christos Ouzounis
- Biological Computation and Process Laboratory, Centre for Research and Technology Hellas, Chemical Process and Energy Resources Institute, Thessalonica, 57001, Greece
| | - Taku Tokuyasu
- Shenzhen Institutes of Advanced Technology, Institute of Synthetic Biology, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, China
| | - Jean-Daniel Zucker
- Integromics, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, 47 Boulevard de l'Hôpital, 75013, Paris, France
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