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Pathak RK, Kim JM. Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare. Brief Bioinform 2024; 25:bbae025. [PMID: 38343323 PMCID: PMC10859662 DOI: 10.1093/bib/bbae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/02/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
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Kim JM, Pathak RK. Editorial: Vetinformatics: an insight for decoding livestock systems through in silico biology. Front Vet Sci 2023; 10:1292733. [PMID: 38026650 PMCID: PMC10643129 DOI: 10.3389/fvets.2023.1292733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Republic of Korea
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Pathak RK, Kim JM. Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle. J Biol Eng 2023; 17:59. [PMID: 37752501 PMCID: PMC10523694 DOI: 10.1186/s13036-023-00378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance. RESULTS In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. CONCLUSION Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea.
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Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences. Mamm Genome 2023; 34:364-378. [PMID: 37076585 PMCID: PMC10382347 DOI: 10.1007/s00335-023-09992-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, 27517, USA
| | - Meghan A Balk
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Robyn L Ball
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | | | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Laura W Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Nicole Vasilevsky
- Data Collaboration Center, Critical Path Institute, Tucson, AZ, 85718, USA
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Mishra DC, Bhati J, Yadav S, Avashthi H, Sikka P, Jerome A, Balhara AK, Singh I, Rai A, Chaturvedi KK. Comparative expression analysis of water buffalo ( Bubalus bubalis) to identify genes associated with economically important traits. Front Vet Sci 2023; 10:1160486. [PMID: 37252384 PMCID: PMC10213454 DOI: 10.3389/fvets.2023.1160486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/11/2023] [Indexed: 05/31/2023] Open
Abstract
The milk, meat, skins, and draft power of domestic water buffalo (Bubalus bubalis) provide substantial contributions to the global agricultural economy. The world's water buffalo population is primarily found in Asia, and the buffalo supports more people per capita than any other livestock species. For evaluating the workflow, output rate, and completeness of transcriptome assemblies within and between reference-free (RF) de novo transcriptome and reference-based (RB) datasets, abundant bioinformatics studies have been carried out to date. However, comprehensive documentation of the degree of consistency and variability of the data produced by comparing gene expression levels using these two separate techniques is lacking. In the present study, we assessed the variations in the number of differentially expressed genes (DEGs) attained with RF and RB approaches. In light of this, we conducted a study to identify, annotate, and analyze the genes associated with four economically important traits of buffalo, viz., milk volume, age at first calving, post-partum cyclicity, and feed conversion efficiency. A total of 14,201 and 279 DEGs were identified in RF and RB assemblies. Gene ontology (GO) terms associated with the identified genes were allocated to traits under study. Identified genes improve the knowledge of the underlying mechanism of trait expression in water buffalo which may support improved breeding plans for higher productivity. The empirical findings of this study using RNA-seq data-based assembly may improve the understanding of genetic diversity in relation to buffalo productivity and provide important contributions to answer biological issues regarding the transcriptome of non-model organisms.
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Affiliation(s)
- Dwijesh Chandra Mishra
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Jyotika Bhati
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Sunita Yadav
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Himanshu Avashthi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Poonam Sikka
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Andonissamy Jerome
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Ashok Kumar Balhara
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Inderjeet Singh
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Krishna Kumar Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
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Stefancsik R, Balhoff JP, Balk MA, Ball R, Bello SM, Caron AR, Chessler E, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525742. [PMID: 36747660 PMCID: PMC9900877 DOI: 10.1101/2023.01.26.525742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Meghan A. Balk
- National Ecological Observatory Network, Battelle, Boulder, CO 80301, USA
| | - Robyn Ball
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Anita R. Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Laura W. Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A. McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Nicole Vasilevsky
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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