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. 2018 May 7:12:1135-1146.
doi: 10.2147/DDDT.S163476. eCollection 2018.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

Meena Kishore Sakharkar  1 , Karthic Rajamanickam  1 , Ramesh Chandra  2 , Haseeb A Khan  3 , Abdullah S Alhomida  3 , Jian Yang  1
Affiliations

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

Meena Kishore Sakharkar et al. Drug Des Devel Ther. .

Abstract

Background: Bovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.

Materials and methods: We have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.

Results: We have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials - oxytetracycline.

Conclusion: This approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.

Keywords: BRD; differential genome analyses; druggability; drugs; pathogenic bacteria; prioritization; targets.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Flow chart detailing the methodology used for the identification of 107 druggable targets from bovine respiratory disease-associated pathogens. Abbreviations: DEG, Database of Essential Genes; GO, gene ontology; KAAS, KEGG Automated Annotation Server; KEGG, kyoto encyclopedia of genes and genomes; VFDB, Virulence Factor DataBase; BLASTP, Basic Local Alignment Search Tool for Proteins.
Figure 2
Figure 2
Venn diagram showing the shared essential genes and putative druggable targets across the three genomes.
Figure 3
Figure 3
The distribution of the 107 drug targets into the major metabolic pathways based on KAAS analysis. Abbreviations: KAAS, KEGG Automated Annotation Server; KEGG, kyoto encyclopedia of genes and genomes; TCA, tricarboxylic acid.
Figure 4
Figure 4
The frequency and distribution of druggable proteins in different cellular pathways based on gene ontology analyses: (A) biochemical processes, (B) molecular function, and (C) subcellular compartments. Abbreviations: UMP, Uridine Monophosphate; IMP, Inosine Monophosphate; NAD, Nicotinamide adenine dinucleotide; FMN, Flavin mononucleotide.
Figure 4
Figure 4
The frequency and distribution of druggable proteins in different cellular pathways based on gene ontology analyses: (A) biochemical processes, (B) molecular function, and (C) subcellular compartments. Abbreviations: UMP, Uridine Monophosphate; IMP, Inosine Monophosphate; NAD, Nicotinamide adenine dinucleotide; FMN, Flavin mononucleotide.
Figure 5
Figure 5
The distribution of the drug targets on the basis of their subcellular localization based on prediction of protein sorting signals and localization sites analysis.
See this image and copyright information in PMC

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