Javascript is currently disabled in your browser. When javascript is disabled, some functions of this website will not work.

Open access for scientific and medical research

From submission to the first editing decision.

From editor acceptance to publication.

The above percentage of manuscripts have been rejected in the past 12 months.

Open access to peer-reviewed scientific and medical journals.

Dove Medical Press is a member of OAI.

Batch reprints for the pharmaceutical industry.

We provide real benefits for authors, including fast processing of papers.

Register your specific details and specific drugs of interest, and we will match the information you provide with articles in our extensive database and send you a PDF copy via email in a timely manner.

Back to Journal »Drug Design, Development and Treatment» Volume 12

Author Sakharkar MK, Rajamaniickam K, Chandra R, Khan HA, Alhomida AS, Yang J

Published on May 7, 2018, Volume 2018: 12 pages, 1135-1146 pages

DOI https://doi.org/10.2147/DDDT.S163476

Single anonymous peer review

Editor approved for publication: Dr. Deng Tuo

Meena Kishore Sakharkar,1 Karthic Rajamanickam,1 Ramesh Chandra,2 Haseeb A Khan,3 Abdullah S Alhomida,3 Jian Yang1 1 School of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada; 2University of Delhi, India Department; 3 Department of Biochemistry, Faculty of Science, King Saud University, Riyadh, Saudi Arabia. Background: Bovine respiratory disease (BRD) is a major problem in cattle production and can cause huge economic losses. The cause of BRD is multifactorial, multimicrobial, and some pathogenic agents are unknown. Therefore, primary management practices, such as post-control antimicrobial injections for BRD prevention, are used to reduce the incidence of BRD in cattle on feedlots. However, this poses a serious threat to the development of antimicrobial resistance, and there is an urgent need to find new interventions that can significantly reduce the impact of BRD and delay/prevent bacterial resistance. Materials and methods: We adopted a subtractive genomics approach to help delineate the essential, host-specific, and drugable targets of the pathogens that cause BRD. We also proposed antimicrobial agents that can be repositioned for BRD in the FDA Green Book and Orange Book. Results: We have identified 107 basic, selective, and druggable hypothetical targets. We also confirmed the sensitivity of two BRD pathogens to one of the proposed antibacterial agents, oxytetracycline. Conclusion: This method allows the relocation of drugs known to other infections to BRD, predicts new drug targets for BRD infection, and provides a new direction for the development of more effective BRD treatments. Keywords: BRD, pathogenic bacteria, targets, drugs, prioritization, differential genome analysis, druggability

The most common infectious disease experienced by breeders, producers and feedlot cattle is bovine respiratory disease (BRD). 1 BRD has harmful effects on the health and production performance of cattle, resulting in considerable economic losses. 2-6 BRD is caused by a variety of factors, including the combination of bacteria and viruses. 7,8 Environmental and stress-related exposures (such as weaning and transportation) and other related factors also have an impact. 9-14 According to data provided by the Canadian Cattle Breeding Association, BRD accounts for 65%-80% of diseases on some feedlots, 45%-75% of death losses, and approximately US$60-750 million in losses to the North American beef industry each year. 15 The main pathogens of BRD are Mannheimia haemolyticus, Haemophilus and Pasteurella multocida from Canada. 16,17 Although not a major problem, these pathogens have also been reported to be associated with other infections in cattle, such as mastitis. 18,19 The results of vaccination are inconsistent.20 Therefore, the main management measures, such as post-protective antibiotic injections to prevent BRD, are used to reduce the incidence of BRD in cattle farms. 21 These measures may lead to antimicrobial resistance (AMR), which, in turn, reduces the effectiveness of antibacterial drugs commonly used to control infectious diseases in cattle. 22 To this end, it is necessary to formulate an effective BRD treatment plan, reduce the use of antibacterial drugs, and effectively control the development of drug resistance. twenty three

An important step in the development of any new treatment method is target identification and early verification. 22 There is a large amount of data on the genome of pathogenic bacteria, and genomics can be used to evaluate the suitability of potential targets using two criteria of "necessity" and "selectivity". 24-26

The target must be critical to the growth, replication, viability, or survival of the microorganism, that is, encoded by genes that are critical to the disease-causing life stage. 27 The basic genes that form the basis of microbial life are essential for survival and, therefore, are likely to be common and conserved among all bacterial species. 28-31 Disruption of these genes can lead to death, making them attractive drug targets.

The microbial target used for treatment should not have any conserved homologs in the host, that is, it should be "selective" to minimize cytotoxicity problems. 24,28 Therefore, only genes are present in the disease-causing genome, but not in the host genome that can be used as candidate drug targets. In terms of selectivity, subtractive genomic analysis has been used as an important technique and has helped target identification and selection of several pathogens. 32-36 This will help avoid costly dead ends when identifying and studying the main targets in detail. At a later stage, it was discovered that all its identified inhibitors had off-target effects in the host. This method, combined with the ability to predict essential genes, can help determine potential targets for drug development. 24,28

The druggability of the target is another priority filter, which determines the potential of the priority target to be regulated by small molecule drugs. 37 This is important because the complete proteomic data of several pathogens is supplemented by genetic necessity data and drug data for these pathogens. Pathogens and their respective mechanisms of action may help determine basic drug targets.

Here, we show the unprecedented potential of complementary data sets (gene necessity, subtractive genomics, and druggability) in prioritizing drug targets. We use genome matching technology to identify pathogen-specific proteins and use the Essential Gene Database (DEG), which provides genetic essential data for 46 bacterial pathogens to select essential genes. The DrugBank database is a resource that combines detailed drug data with comprehensive drug target information, and is used to assign drug properties to candidate targets. 38 The above-mentioned database and the fully sequenced genome data of Bos taurus and BRD pathogens provide the basis for using selectivity, necessity, and druggability as the standard, and solve the complex and difficult problems in target prioritization through computational methods. In order to better understand the cellular organization and function of priority targets, we also analyzed the protein pathways and gene ontology (GO) of these targets. 39 Our results will be very useful for the development of more effective BRD treatment strategies, such as new drug development and drug repositioning.

The proteome of the host B. taurus and key BRD pathogens – H. somni strain 2336 (NC_010519.1), Hemolytic Mycoplasma strain M42548 (NC_021082.1) and Plasmodium multicidal strain 36950 (NZ_CP008918) – have been downloaded. (October 20, 2017) From the website of the National Center for Biotechnology Information (NCBI). The proteome of H. somni, M. helytica and P. multocida has 1,957, 2,833 and 1,856 proteins, respectively. The B. taurus proteome has 49,107 proteins (Table 1).

Because the genetic necessity data of BRD pathogens is not available, the proteome of BRD pathogens is based on the expected value (E value) cut-off value and bit score of 10-10 to perform basic partial comparison of proteins against the DEG protein database. The search tool is >100 to identify that it may be An essential protein, so it may be a drug target. The E value describes the number of clicks that can be "expected" by chance when searching the database.

In order to minimize the drug cross-reactivity caused by binding to homologous proteins in the host, BLASTP analysis of B. taurus proteome was performed on all three pathogens involved in BRD, and the E value cut-off value was 10- 4. Position score>100 excludes host proteins that are similar to pathogen proteins. This will help to screen out proteins that are essential to BRD pathogens but have B. taurus homologs.

The druggability of screening proteins was studied for all drug targets existing in the DrugBank database. The database contains 8,261 drug entries, including small molecule drugs approved by the FDA (U.S. Food and Drug Administration) in 2021, and 233 biologics approved by the FDA. Technical (protein/peptide) drugs, 94 types of nutritional products and more than 6,000 experimental drugs. 40 In addition, 4,338 non-redundant protein (ie drug target/enzyme/transporter/carrier) sequences are associated with these drug entries. The results from these two searches were BLASTP hits, with a bit score of >100, and an E-value cut-off value of <10-5, which was considered a potential candidate for drug treatment. We also performed a BLASTP search on the drug targets of 85 bovine gut microbial genome sequences, with a cut-off value of 10-100 and a bit score >100 to identify targets that did not match in the gut microbiome (data not shown) ).

Then quantitative analysis of drug targets, such as pathway analysis, GO analysis, blocking point analysis, protein sorting signal prediction and localization site (PSORT) biolocation analysis, virulence analysis and AMR analysis. Download the list of drugs approved for BRD from the FDA Green Paper as a positive control.

Drugs combined with the predicted drug targets identified above (data set 1) are also matched with the FDA Orange Book and FDA Green Book to determine the safety and effectiveness of drugs approved under the Federal Food, Drug, and Cosmetic Act ( Act) and related patent and exclusive information (Table 2).

Table 2 List of drugs for bovine respiratory diseases predicted in the FDA Green Book and FDA Orange Book (315) Note: The drugs approved for use in animals in the Orange Book are shown in parentheses. Abbreviations: FDA, US Food and Drug Administration; veterinarian, veterinarian.

Analyze putative drug targets through KAAS (Kyoto Encyclopedia of Genes and Genomes [KEGG] Automatic Annotation Server) to obtain data on biological processes and metabolic pathways. 41 KAAS performed a BLASTP comparison with the KEGG gene database and provided functional annotations for the target protein.

The BioCyc database was used to conduct choke point analysis on the metabolic pathways of the organisms that cause BRD. Downloaded a list of the various proteins involved in choke point reactions and catalyzing reactions. 41 The list of hypothetical targets for druggable drugs was screened against this list of blocking points to determine that there are no alternative targets available. 42

GO-Use Uniprot web server (http://www.uniprot.org/) to assign biological and molecular functions and cell distribution to priority targets. 43

The PSORT beta (PSORTb) server is used to predict the subcellular location of putative drug targets to analyze the location of these targets in different compartments of the cell. 44

The Virulence Factor Database (VFDB) (http://www.mgc.ac.cn/VFs/) is a public resource that provides the latest knowledge about several bacterial pathogen virulence factors (VF). 45 BLASTP for VFDB is a cut-off value of 10-5 and a bit score of >100 to identify putative VF from selected BRD pathogenic organisms.

Oxytetracycline (Alpha Acer, Canada) is a drug that targets our presumed druggable target and has been approved by the FDA. Therefore, we chose it as a drug to verify our established goals in vitro. The experiment was performed in duplicate. The bacterial strains of M. haemolytica ATCC 29702 and P. multocida ATCC 43137 were purchased from CEDARLANE Corporation (Burlington, ON, Canada) and revived according to the manufacturer's instructions. A 96-well plate was used to determine the antimicrobial susceptibility to oxytetracycline. The concentration of oxytetracycline used was 8, 4, 2, 1, and 0.5 μg/mL. The pure subculture was inoculated in brain heart infusion broth and incubated overnight at 35°C. Then adjust the bacterial suspension to 0.5 McFarland turbidity standard according to the manufacturer's instructions. Then add the bacterial suspension (100 μL) to each well. The plate was incubated at 35°C for 24 hours and then read using a 96-well plate reader (BIORAD iMark Microplate Reader 655 nm; Table 3).

Table 3 Sensitivity of two bovine respiratory disease-related pathogens to oxytetracycline

Figure 1 and Figure 2 respectively show the processes shared by the three pathogens and the flow chart of essential genes and drug targets.

The flowchart in Figure 1 details the method used to identify 107 druggable targets from pathogens associated with bovine respiratory diseases. Abbreviations: DEG, essential gene database; GO, gene ontology; KAAS, KEGG automatic tagging server; KEGG, Kyoto Encyclopedia of Genes and Genomes; VFDB, virulence factor database; BLASTP, basic partial comparison search tool for proteins.

Figure 2 Venn diagram showing essential genes and putative drug targets shared in the three genomes.

Here, we show the results of a new method of delineating target identification in the BRD drug discovery process.

The proteome of H. somni, Mycoplasma hemolyticus, and Plasmodium vulgaris were identified as essential proteins by BLASTP against DEG, and 1,089, 1,246 and 1,255 essential proteins were identified respectively. Due to their important role in the various pathways of pathogen survival, these genes are highly conserved among different populations and species. 46,47

Subtractive genomics, druggability prediction and FDA matching

An effective and fast method to identify targets that are selective for pathogenic species but not present in the host genome is computer subtractive genome analysis. BLASTP analysis was performed on the above-mentioned essential proteins identified for the B. taurus proteome, and 821, 951, and 964 proteins were identified in the genomes of H. somni, Mycoplasma hemolyticus, and Plasmodium multocida. They were not significantly related to any host protein. To match. Of these proteins, 62, 71, and 39 were found to be hypothetical or unknown in their respective genomes, so no further analysis was considered. Based on sequence similarity, the remaining 759, 880, and 925 essential proteins were identified by BLASTP against the Drugbank database, and 204, 230, and 240 proteins were identified, respectively, which can be used for Haemophilus Haemophilus, Plasmodium haemolyticus, and Haemophilus multicidalis. Bacillus. In addition, 107 proteins were identified as conserved in all three bacteria that cause BRD and require further analysis (Figure 2). These 107 proteins were targeted by 315 drugs. Surprisingly, there are no currently approved BRD drugs on this list. This is because BRD drugs are not in the DrugBank compendium. We further matched these 315 drugs with the FDA Orange Book and Green Book (https://www.fda.gov/; access time is December 20, 2017) to determine the safety of FDA in accordance with federal regulations. On the basis of the validity and validity of the Drug Food, Drug and Cosmetic Act and related patents and exclusive information. Our 32 drugs were found in the FDA Orange Book and 10 were found in the FDA Green Book (Table 2).

Path analysis, GO, choke point and VF

KAAS: Using KAAS to conduct pathway analysis on 107 common priority drug targets, 62 pathway annotations were found. 29 The distribution of these 107 proteins in different pathways is shown in Figure 3. Most proteins are components of ribosomes (15), involved in amino acid biosynthesis (15), or involved in pyrimidine metabolism (11). GO: We analyzed the GO terms of 107 priority target proteins (Figure 4). Analysis shows that translation (17), cell shape regulation (11), cell wall organization (11) and peptidoglycan biosynthesis (11) are the most common biological processes. A total of 129 classifications have been identified, indicating that several proteins are involved in more than one biological process. A total of 149 classifications have been identified under molecular functions. The three most abundant are ATP binding (20), metal ion binding (19) and ribosomal structural components (16). These proteins are classified in 26 locations in bacterial cells, but are mainly distributed in the cytoplasm. Blocking point: We performed blocking point analysis on 107 potential target proteins to identify blocking point proteins. The lack of alternative approaches can almost perfectly predict necessity. 48 Of the 107 common drug targets that were prioritized, 26 were identified as blocking point proteins. Choke point proteins are druggable and effective targets, because the inhibition of these block point proteins is expected to produce a block in this pathway, which may cause unsustainable conditions in bacterial cells. Therefore, these proteins are predicted to be attractive proteins for designing effective inhibitors in their respective pathways. VFs: Ten proteins have been identified as essential, non-host homologs, druggable and related to virulence. The identified VF is encoded by the following genes: pdxA, rfaD, relA, glmm, gmhA, coaD, lpxC, ispD, kdsA, and kdsB. 43 Some virulence genes are important for the establishment of infection by bacteria and indirectly participate in the pathogenesis. 49 Recently, it has been suggested that antiviral drugs may produce weaker resistance options compared with other antibacterial drugs because they neutralize the pathogen's potential to cause infection, rather than inhibit bacteria or kill bacteria. 50 Nevertheless, resistance to drugs against virulence factors has been reported.

Figure 3 The distribution of 107 drug targets in the main metabolic pathways based on KAAS analysis. Abbreviations: KAAS, KEGG automatic labeling server; KEGG, Kyoto Encyclopedia of Genes and Genomes; TCA, tricarboxylic acid.

Figure 4 The frequency and distribution of druggable proteins in different cellular pathways based on gene ontology analysis: (A) biochemical process, (B) molecular function, and (C) subcellular compartment. Abbreviations: UMP, uridine monophosphate; IMP, inosine monophosphate; NAD, nicotinamide adenine dinucleotide; FMN, flavin mononucleotide.

Determining the subcellular location of proteins, especially in the case of disease-causing species, helps reveal their involvement in pathogenesis. 51 Proteins that are susceptible to any form of external intervention (such as cell wall and cell membrane proteins) are considered more effective. A drug target that is more attractive than cytoplasmic proteins. Figure 5.41 depicts the distribution of predicted subcellular locations based on PSORTb's 107 putative drug targets. 100 of a total of 107 (~93%) proteins were predicted to be cytoplasmic proteins, and two of these proteins were found to be cytoplasmic membrane proteins. Cytoplasmic proteins are involved in many important metabolic processes, so they are very important to the physiology of bacteria. In addition, most cellular activities occur in the cytoplasm. Peng and Gao also reported that the cytoplasm is rich in essential proteins. 52 Drug delivery strategies that include the use of nanoparticles, cell-penetrating peptides, pH-responsive carriers, and endosomal disruptors may help overcome the cell membranes in these Gram-negative bacteria that prevent drugs from entering cytoplasmic targets. PSORTb cannot specify the subcellular locations of the five proteins. What needs to be explained here is that PSORTb is an algorithm based on support vector machines, and its prediction accuracy depends on the training set. Sometimes, it may predict multiple locations of a protein and assign it to multiple locations, or it may not be able to predict or assign the protein to any location in the cell.

Figure 5 Subcellular localization of drug target distribution based on protein sorting signal prediction and localization site analysis.

Most of the drugs in our analysis target one protein. The drug DB08185 targets 10 proteins, all of which are part of small ribosomal subunits. Gene gyrA and gene parC are both targeted by 18 drugs, of which 17 drugs are shared. These drugs are DB00218-moxifloxacin (fluoroquinolone-FDA); DB00365-grepafloxacin (quinolones-withdrawn), DB00467-enoxacin (6-fluoronaphthyridone-FDA), DB00487-pefloxacin ( Fluoroquinolone – FDA), DB00685 – Travafloxacin (Fluoroquinolones – Withdrawn), DB00485 – Travafloxacin (Fluoroquinolones – Withdrawn), DB000 Quinolones – 37 Fluoroquinolones), DB000 Quinolones Drugs-37 fluoroquinolone drugs), DB000C4 fluoroquinolone (FDA), DB0000C4 fluoroquinolone (FDA) (fluoroquinolone-FDA), DB00978-lomefloxacin (fluoroquinolone-FDA), DB01059-norfloxacin (fluoroquinolone) -FDA), DB01137-Levofloxacin (fluoroquinolone-FDA), DB01155-Gemifloxacin (fluoroquinolone-FDA), DB01155-Gemifloxacin (fluoroquinolone-FDA), DB01208-Sparfloxacin (fluoroquinolone-FDA) ), DB01405-temafloxacin (fluoroquinolone-withdrawn), DB04576-fleroxacin (fluoroquinolone-FDA), DB06771-besifloxacin (fluoroquinolone-FDA), DB09047-fluoroquinolone (FDA). DB00827-cinoxacin (synthetic antibacterial agent related to oxazole acid and nalidixic acid-withdrawn)-only for gyrA instead of parC and DB00817-rosoxacin (quinolone derivative antibacterial agent-FDA) only for parC and not gyrA.

According to observations, both Mycoplasma hemolyticus and Plasmodium multocida are sensitive to the antibacterial oxytetracycline (Table 3).

BRD is a major threat related to cattle morbidity and mortality, and an important issue in cattle production. In North America, an estimated US$54.12 million is used to treat respiratory diseases in cattle each year. This does not include production losses due to morbidity and mortality. 1,3,4,6 Exposure to various physical and physiological stress factors and certain viral infections can make cattle susceptible to BRD. 53 Several factors can contribute to BRD, including but not limited to farm environment, social and relocation challenges, and complex interactions between the host immune system and pathogens. 53-55 Since the effective treatment of the disease depends on accurate diagnosis and underestimation of the factors that cause the disease, the efficiency of vaccination and antibacterial treatment against BRD-related bacteria is inefficient. 2,9,56,57

Antibiotic allergy is a large-scale medical treatment of a group of animals to eliminate or minimize the expected outbreak. Metaphylaxis has been shown to reduce the morbidity and mortality of feedlots and breeders who reach high-risk cattle that are considered to have clinical signs of BRD. 55,58-63 However, the inability to subjectively identify, pull and treat sick cattle that arrive at the feedlot or storage facilities poses a serious threat to the development of AMR. There is an urgent need to find new types that may significantly reduce the impact of BRD and delay/prevent bacterial resistance Intervention measures. According to reports, Metaphylaxis causes multi-drug resistance, but this has not been investigated in detail. 58,64

Here, it’s worth noting that since BRD usually involves a combination of pathogens, and it is impractical to determine the specific pathogen populations present in the newly arrived cattle on the farm, the newly identified/developed drug candidates should be targeted at one that can be used in the entire process. The target seen in. There are many kinds of bacteria. Essential genes have a lethal phenotype, and the proteins encoded by them are important for the growth and survival of organisms. They are also highly conserved in organisms, and therefore provide a viable target for drugs trying to target a wide range of bacteria. 29–31,65 It is worth mentioning that although the selectivity of known targets and the lack of targets in the host genome reduce the chance of side effects, these standards are difficult to truly achieve. 25 This is because there are too many beneficial bacteria and archaea in the rumen microbiota, and drugs are likely to target proteins encoded by essential genes in the gut microbiome. Consistent with this, the BLASTP matching of our priority 107 targets with 85 bovine gut bacteria and archaea genomes (identified from NCBI and Hunmicrobiome) showed that all 107 proteins have significant matches, with a cutoff value of 10-100 Or lower (data not shown). This again emphasizes the fact that antibiotics need to be used with caution and only when needed to treat cattle infections, as they may affect/change the tumor gastrointestinal flora. 66

Drug-readiness-the possibility of being able to regulate the target through drugs is essential to determine whether a drug discovery project progresses from "hit" to "leading", because drug-readiness information guidance is aimed at providing better prospective protein drug discovery work . 67 In view of this, the prediction of druggability is important to avoid costly dead ends and difficult targets. In the current study, we used genomics to prioritize drug targets to describe the three key BRD pathogens H. somni, M. helytica and P. multocida's new basic drug targets. A final list of 107 basic and medicable targets was obtained. These goals are involved in basic biological processes (Figures 2 and 3). According to the Drugbank database, there are 315 drugs available for these targets. Some drugs target multiple pathways. However, 248 of the 315 drugs are single-targeted drugs, and 40 of the 107 targets are single-drug targeted. The proteins encoded by the genes gyrA and parC are targeted by the largest number of drugs and also share the largest number of drugs that target them. Most drugs are quinolones and their derivatives. They are reported to target the essential bacterial enzymes DNA gyrase (gyrA) and topoisomerase IV (parC) because these enzymes have a high degree of sequence identity and structural homology sex. Unfortunately, none of the drugs currently used to treat BRD infections in cattle (Table 2) are present in these 315 drugs. A thorough inspection of the DrugBank database revealed that although it has several drugs for veterinary purposes, there is a lack of drugs for BRD in the database. However, it must be emphasized that the compilation of BRD drugs and their mechanisms of action from the FDA Green Book and other documents shows that the key targets are DNA gyrase, 50S ribosomal subunits, and cell wall synthesis inhibitors (Table 2). Interestingly, the 10 drugs on our list are in the FDA Green Book and approved for veterinary use under DrugBank, which can be re-used for BRD infections in cattle. Some of the drugs in the FDA Orange Book are approved for veterinary use and can be safely used to treat cattle infections. It is worth mentioning that antibiotics used in human medicine may not be a good choice, and may not even be approved for the treatment of BRD and other veterinary infections, because the use of important drugs in human medicine in food animals has become a kind of Trend. Consistent with this, it is worth mentioning that toxicity issues are responsible for nearly 30% of the failure of drug development programs. 68 Based on our results, we recommend that the 10 and 3 antibiotics in the FDA Green Book and Orange Book (approved for veterinary use), respectively, be reused for BRD treatment (Table 2). In addition, because these drugs are on the FDA and Drugbank lists and have known pharmacokinetics and safety characteristics, as drug development targets, they have a greater chance of success, bring less risk, and are economical and economical. time consuming. In addition, it is worth mentioning that <15% of compounds entering clinical development are approved, and drug reuse helps us overcome this limitation in some way. 69,70 In order to delineate the drug development (including reuse) opportunities generated by these analyses, we connected information on multiple data sets (such as choke point, virulence, and GO) to the conservative and basic goals of these druggable drugs.

Oxytetracycline has been approved for the treatment of pneumonia and BRD associated with Pasteurella. 71 Our current findings are consistent with previous reports that oxytetracycline is also effective against Mycoplasma hemolyticus (Table 3). 72 The experimental validation of other FDA drugs on BRD pathogens and BRD pathogens produces genomes that produce the desired results, which may help overcome the important problems of AMR.

In addition, it is worth mentioning that the FDA-approved drug information targeting multiple pathways can help us identify drugs that can be used in combination to treat BRD and other bacterial infections.

Need to mention some limitations of our analysis, which may make our target and drug list incomplete. First, the terminology is inconsistent across data sets. Second, the current list of drug targets may not be comprehensive because the criteria used to select targets are multiple and strict. Third, the list of drugs in the drug database may not be comprehensive. 73 Finally, the essential genes required for growth on different media may be different, so our list is controversial. In addition, different methods may lead to different test results. For example, in the transposon mutagenesis method, genes that only slow down growth may be mistakenly selected as essential genes. Essential genes that exist in multiple copies may also be misclassified. 74-77 Extracellular proteins may sometimes be critical to the survival of pathogens in the laboratory. However, since they are not usually necessary for the survival of pathogens, they are not present in the list of targets determined based on homology with known essential proteins. We did not include these targets in our list because extracellular proteins have been reported to evolve faster and are therefore more likely to mutate and promote the development of drug resistance. This analysis does not consider the long-distance gene-protein relationships that may be missed, because the comparison score may have low statistical significance for these long-distance relationships. In addition, genes that do not have homologs in DEG may be missed. This is because DEG contains essential genes determined through genome-wide essentiality screening experiments, and this data is only applicable to <100 pathogens.

One possible solution to overcome these limitations is to improve annotation across data sets and to integrate and unify the various data sets. In addition, over time, the increase in genome-wide necessity screening of various pathogens will help improve the prediction of genetic necessity.

But the drug target space and the drug space are not all-encompassing, and they are constantly expanding. The data provided here shows that using simple biological criteria to gradually prioritize proteins is an effective way to quickly reduce the number of targets of interest to an experimentally manageable number. This process is an effective method to enrich potential target genes and identify genes that are essential for normal cell function.

This work was supported by the Saskatchewan Government and the Agricultural Development Fund (ADF) of SaskMilk and MITACS. Canada’s grants to MKS and JY HAK are thanks to the Director of the Institute of Science at King Saud’s University (Saudi Arabia) for Funded research group number RGP-009.

The authors report no conflicts of interest in this work.

Griffin D. Economic impact associated with respiratory diseases in beef cattle. Vet Clin North Am Food Animation Practice. 1997;13(3):367-377.

Larson R. The effect of cattle disease on carcass traits. J Animation Science. 2005; 83 (13 supplement): E37-E43.

Smith RA. The impact of disease on feedlot performance: a review. J Animation Science. 1998;76(1):272-274.

Wittum TE, Woolen NE, Perino LJ, Littledike ET. The relationship between the treatment of respiratory diseases, obvious lung lesions at slaughter, and the rate of weight gain of cattle on feedlots. J Am Vet Med Assoc. 1996;209(4):814-818.

Rajala-Schultz PJ, Torres AH, Degraves FJ, Gebreyes WA, Patchanee P. Antimicrobial resistance and genotypic characteristics of coagulase-negative staphylococci during drought. Veterinary microorganisms. 2009;134(1–2):55–64.

Johnson KK, Pendle DL. Reduce the market impact of the prevalence of respiratory diseases in beef cattle farms in the United States. Former veterinary science. 2017; 4:189.

Headley SA, Okano W, Balbo LC, etc. Molecular investigation of infectious factors associated with bovine respiratory diseases in beef cattle farms in southern Brazil. J Investment in veterinary diagnostics. 2017;30(2):249–251.

Holman DB, Klima CL, Ralston BJ, etc. Metagenomic sequencing of bronchoalveolar lavage fluid samples from feedlot cattle mortality related to bovine respiratory diseases. Genome announced. 2017;5(40):e01045-17.

Gagea MI, Bateman KG, van Dreumel T, etc. Diseases and pathogens associated with mortality in beef farms in Ontario. J Investment in veterinary diagnostics. 2006;18(1):18-28.

Haines DM, Martin KM, Clark EG, Jim GK, Janzen ED. Immunohistochemical detection of Mycoplasma bovis and bovine viral diarrhea virus in the tissues of cattle with chronic, unresponsive respiratory diseases and/or arthritis. Can Vet J. 2001;42(11):857.

Jensen R, Pierson R, Brady P, etc. Transport febrile pneumonia in one-year-old fattening cattle. J Am Vet Med Assoc. 1976;169(5):500–506.

Shahriar FM, Clark EG, Janzen E, West K, Wobes​​er G. Chronic pneumonia feedlot cattle are co-infected with bovine viral diarrhea virus and mycoplasma. Can Vet J. 2002;43(11):863.

Welsh Road, Dye LB, Payton I, awarded AW. Isolation and antibacterial sensitivity of the bacterial pathogen of bovine pneumonia: 1994-2002. J Investment in veterinary diagnostics. 2004;16(5):426–431.

Hoerlein A, Marsh C. Epidemiological study of shipping fever in calves. J Am Vet Med Assoc. 1957; 131(3): 123-127.

Maday J. goes back to the foundation of BRD. Canadian Herders Beef Magazine. December 2014: 1-4.

Apley M. Bovine Respiratory System Disease: Pathogenesis, Clinical Symptoms and Treatment of Lightweight Calves. Vet Clin North Am Food Animation Practice. 2006;22(2):399-411.

Ellis JA. Immunology of Bovine Respiratory Disease Complex. Vet Clin North Am Food Animation Practice. 2001; 17(3): 535–550, vi–vii.

Turni C, Dayao D, Aduriz G, etc. Strains of Pasteurella multocida affect the heifers and calves in different ways. Veterinary microorganisms. 2016;195:17-21.

Blum S, Freed M, Zukin N, etc. Bovine subclinical mastitis caused by Mannheim’s granulomatosis. J Investment in veterinary diagnostics. 2010;22(6):995–999.

Larson RL, step DL. Evidence-based effectiveness of vaccination against Mannheimia hemolyticus, Pasteurella multocida and sleepiness tissue in cattle in feedlots to reduce the incidence and impact of bovine respiratory diseases. Vet Clin North Am Food Animation Practice. 2012;28(1):97–106, 106e101–107, ix.

Checkley SL, Campbell JR, Chirino-Trejo M, Janzen ED, Waldner CL. The association between antibiotic use and the prevalence of antimicrobial resistance in E. coli in the feces of cattle in feedlots in western Canada. Can Vet J. 2010;51(8):853-861.

Lindsay horse. Target discovery. Nat Rev drug discovery. 2003;2(10):831–838.

Windeyer C, Timsit E, Barkema H. ​​Respiratory diseases in pre-weaning cows: Are current prevention strategies good enough? Veterinarian J. 2017; 224:16-17.

Perumar D, Lim CS, Sakharkar KR, Sakharkar MK. Differential genome analysis of metabolic enzymes of Pseudomonas aeruginosa was used for drug target identification. In silica bio. 2007;7(4, 5):453–465.

Sakharkar KR, Sakharkar MK, Chow VT. A new genomics method for identifying drug targets in pathogens, especially Pseudomonas aeruginosa. In silica bio. 2004;4(3):355–360.

Hossain MU, Khan MA, Hashem A, etc. Through proteomic exploration to find potential therapeutic targets for Shigella flexneri. Former microorganisms. 2016; 7:1817.

Hadizadeh M, Tabatabaiepour SN, Tabatabaiepour SZ, Hosseini Nave H, Mohammadi M, Sohrabi SM. Whole genome identification of potential drug targets in Enterobacteriaceae: a method based on homology. Microbial resistance. 2017;24(1):8-17.

Mushegian AR, Koonin EV. The smallest gene set for cell life obtained by comparing the complete bacterial genome. Proc Natl Acad Sci US A. 1996;93(19):10268-10273.

Glass JI, Assad-Garcia N, Alperovich N, etc. Essential genes for the smallest bacteria. Proc Natl Acad Sci US A. 2006;103(2):425-430.

Jordan IK, Rogozin IB, Wolf YI, Koonin EV. Essential genes are evolutionarily more conservative than non-essential genes in bacteria. Genome research. 2002;12(6):962-968.

Liao BY, Scott NM, Zhang J. The influence of gene importance, expression pattern and gene compactness on the rate of mammalian protein evolution. Molecular biology evolution. 2006;23(11):2072-2080.

Vadud A, Jamal A, Riaz M, etc. Subtractive genome analysis for computer identification and characterization of new drug targets of Streptococcus pneumoniae strain JJA. Microbial pathogens. 2017; 115: 194-198.

Uddin R, Siddiqui QN, Azam SS, Saima B, Wadood A. Identification and characterization of extensively drug-resistant Mycobacterium tuberculosis (XDR KZN 605) hypothetical protein as a potential drug target through subtractive genomics. Eur J Pharm Sci. 2018; 114: 13-23.

Jamal SB, Hassan SS, Tiwari S, etc. A comprehensive computer method for identifying therapeutic targets of the human pathogen Corynebacterium diphtheriae. Public Science Library One. 2017;12(10):e0186401.

Birhanu BT, Lee SJ, Park NH, Song JB, Park SC. Using subtractive/comparative genomics methods, computer simulation analysis of putative drugs and vaccine targets in the metabolic pathway of Actinobacillus pleuropneumoniae were performed. J Veterinary Science. 2017;19(2):188-199.

Hossain T, Kamruzzaman M, Choudhury TZ, Mahmood HN, Nabi A, Hosen MI. The application of subtractive genomics and molecular docking analysis in the identification of new putative drug targets against Salmonella enterica subsp. Intestinal serotype Poona. BioMed Res Int. 2017; 2017: 3783714.

Agüero F, Al-Lazikani B, Aslett M, etc. Genome size prioritization of drug targets: TDR target database. Nat Rev drug discovery. 2008; 7(11): 900–907.

Luo H, Lin Y, Gao F, Zhang CT, Zhang R. DEG 10, updated the essential gene database, including protein-coding genes and non-coding genomic elements. Nucleic acid research. 2013; 42(D1): D574–D580.

Singer S, Singer DB, Singer A, etc. A method to identify new drug targets in Streptococcus pyogenes SF370 through pathway analysis. Interdisciplinary science. 2016;8(4):388-394.

Wishart DS, Feunang YD, Guo AC, etc. DrugBank 5.0: A major update of the DrugBank database in 2018. Nucleic Acids Res. 2018; 46(D1): D1074–D1082.

Wishart DS, Wu A. Use DrugBank for computer drug discovery and discovery. Curr Protoc Bioinformatics. 2016; 54: 14.4.1-14.4.31.

Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: automatic genome annotation and pathway reconstruction server. Nucleic acid research. 2007; 35 (Supplement 2): W182-W185.

Caspi R, Altman T, Billington R, etc. MetaCyc database of metabolic pathways and enzymes and BioCyc collection of pathway/genomic databases. Nucleic acid research. 2014; 42 (database issues): D459–D471.

Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acid research. 2000;28(1):27-30.

Shannon P, Markiel A, Ozier O, etc. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498-2504.

Chen L, Zheng D, Liu B, Yang J, Jin Q. VFDB 2016: Hierarchical and refined data sets for big data analysis-10 years. Nucleic acid research. 2016;44(D1):D694–D697.

Scardoni G, Petterlini M, Laudanna C. Use CentiScaPe to analyze biological network parameters. Bioinformatics. 2009;25(21):2857–2859.

Gold AB. The virulence mechanism of bacterial pathogens. The third edition. New Delhi, India: 2000.

Dickey SW, Cheung GYC, Otto M. Different drugs for bad bugs: anti-virus strategies in the era of antibiotic resistance. Nat Rev drug discovery. 2017; 16(7): 457–471.

Peng C, Gao F. Protein location analysis of essential genes in prokaryotes. Scientific Representative 2014; 4:6001.

Acharya A, Garg LC. Drug target identification and prioritization for the treatment of sheep foot rot: a computer method. International J Genomics. 2016; 2016: 7361361.

Duff GC, Galyean ML. The board of directors invites to review: the latest developments in the management of cattle from the newly-accepted feedlot under high pressure. J Animation Science. 2007;85(3):823–840.

Taylor JD, Fulton RW, Lehenbauer TW, Step DL, Confer AW. Epidemiology of bovine respiratory diseases: what is the evidence for the predisposing factors? Can Vet J. 2010;51(10):1095-1102.

McVeigh DS. BRD research needs in the next 10-20 years. Anim Health Res Rev. 2009; 10(2):165–167.

Urban-Chmiel R, Grooms D. Prevention and control of bovine respiratory diseases. J Livestock Science. 2012; 3:27-36.

Johnston D, Earley B, Cormican P, etc. Illumina MiSeq 16S amplicon sequence analysis of lung and mediastinal lymph node tissues of bacteria related to bovine respiratory diseases. BMC veterinary research. 2017;13(1):118.

Van Donkersgoed J. A meta-analysis of field trials of a large number of antimicrobials for the prevention of bovine respiratory diseases in cattle on feedlots. Can Vet J. 1992;33(12):786.

Cameron A, McAllister TA. Antibiotic use and resistance in beef production. J Anim Sci Biotechnol. 2016;7(1):68.

The impact of the extensive use of antibiotics when Prime Minister Cusack entered the feedlot on the health and growth rate of cattle destined for the domestic Australian market. Aust Vet J. 2004;82(3):154-156.

Frank GH, Briggs RE, Duff GC, Loan RW, Purdy CW. The effects of vaccinations before transportation and florfenicol on arrival at the feedlot on the health of transported calves and detection of hemolytic Mannheimia in nasal secretions. Am J Vet Res. 2002;63(2):251–256.

Step DL, Engelken T, Romano C, etc. Evaluation of three antimicrobial options for allergic reactions in raised calves at high risk of developing respiratory diseases in cattle. veterinary. 2007;8(2):136-147.

Wellman NG, O'Connor AM. A meta-analysis of the use of turamycin in the treatment of bovine respiratory diseases. J Vet Pharmacol Ther. 2007;30(3):234–241.

Guthrie C, Rogers K, Christmas R, Vogel G, Laudert S, Mechor G. The efficacy of metaphylactic tilmicosin in controlling respiratory diseases in cattle raised in high-risk northern calves. Cattle practitioner. 2004:46-53.

Owens J. determines the druggability. Nat Rev drug discovery. 2007;6(3):187.

Yeh I, Hanekamp T, Tsoka S, Karp PD, Altman RB. Computational analysis of the metabolism of Plasmodium falciparum: Organizing genomic information to facilitate drug discovery. Genome research. 2004;14(5):917-924.

Catry B, Dewulf J, Maes D, etc. The effect of antibiotic dosage and production type on antimicrobial resistance of bovine respiratory and digestive tract. Public Science Library One. 2016;11(1):e0146488.

Nosengo N. Can you teach new tricks to old medicine? nature. 2016;534(7607):314-316.

Kola I, Landis J. Can the pharmaceutical industry reduce the attrition rate? Nat Rev drug discovery. 2004;3(8):711-715.

Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. The clinical development success rate of the research drug. Nat Biotech. 2014; 32(1): 40–51.

Corsello SM, Bittker JA, Liu Z, etc. Drug reuse center: next-generation drug library and information resources. Nat Medical. 2017;23(4):405–408.

Nobun JE, Scott GR, Tonelli RJ. Overview of drugs approved in FDA publications with therapeutic equivalence assessments. It is J Hosp Pharm. 1990;47(12):2696-2700.

Lees P, Potter T, Pelligand L, Toutain PL. The pharmacokinetic-pharmacodynamic integration and modeling of oxytetracycline on the calf pathogen Mannheimia hemolyticus and Pasteurella multocida. J Vet Pharmacol Ther. 2018;41(1):28-38.

Ma'ayan A, Jenkins SL, Goldfarb J, Iyengar R. Network analysis of FDA-approved drugs and their targets. Mount Sinai J Med. 2007;74(1):27-32.

Juhas M, Eberl L, Church general manager. A basic gene as an antibacterial target and a cornerstone of synthetic biology. Trends in biotechnology. 2012;30(11):601–607.

Juhas M, Eberl L, Glass JI. The essence of life: the essential genes of the smallest genome. Trends in cell biology. 2011;21(10):562–568.

Suthers PF, Zomorrodi A, Maranas CD. Genome-scale gene/reaction importance and synthetic lethality analysis. Moore Systems Biology. 2009; 5:301.

Tucker CL, Fields S. Deadly combination. Nat Ginette. 2003;35(3):204-205.

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and include the Creative Commons Attribution-Non-commercial (unported, v3.0) license. By accessing the work, you hereby accept the terms. The use of the work for non-commercial purposes is permitted without any further permission from Dove Medical Press Limited, provided that the work has an appropriate attribution. For permission to use this work for commercial purposes, please refer to paragraphs 4.2 and 5 of our terms.

Contact Us• Privacy Policy• Associations and Partners• Testimonials• Terms and Conditions• Recommend this site• Top

Contact Us• Privacy Policy

© Copyright 2021 • Dove Press Ltd • Software development of maffey.com • Web design of Adhesion

The views expressed in all articles published here are those of specific authors and do not necessarily reflect the views of Dove Medical Press Ltd or any of its employees.

Dove Medical Press is part of Taylor & Francis Group, the academic publishing department of Informa PLC. Copyright 2017 Informa PLC. all rights reserved. This website is owned and operated by Informa PLC ("Informa"), and its registered office address is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 3099067. UK VAT group: GB 365 4626 36

In order to provide our website visitors and registered users with services that suit their personal preferences, we use cookies to analyze visitor traffic and personalize content. You can understand our use of cookies by reading our privacy policy. We also retain data about visitors and registered users for internal purposes and to share information with our business partners. By reading our privacy policy, you can understand which of your data we retain, how to process it, with whom to share it, and your right to delete data.

If you agree to our use of cookies and the content of our privacy policy, please click "Accept".