<p> Pasteurella multocida is a respiratory pathogen that is frequently isolated from cattle suffering  from bovine respiratory disease (BRD), the leading cause of mortality and morbidity on modern day cattle farms. Treatment involves the use of antimicrobials which have been shown to fail for  about 30% of BRD cases, leading to the suspicion that etiologic agents, such as P. multocida, may  be resistant. Phenotypic resistance can be confirmed via laboratory antibiotic susceptibility testing  (AST) but this requires several days to complete. Genotypic resistance could be quickly assessed  via nucleic acid assays based on the presence of known antibiotic resistance genes (ARGs). In  human medicine, resistant genes associated with common antibiotics (i.e., ampicillin and penicillin)  in common pathogens (i.e., Salmonella) are very accurate in predicting phenotypic resistance;  however, ARGs associated with antibiotics used to treat BRD, such as enrofloxacin and  tulathromycin, have shown low genotype-phenotype concordance. Hence, this study aims to  improve P. multocida genotype-phenotype concordance by applying a machine learning (ML)  algorithm to identify novel genomic sequences (biomarkers) that have greater accuracy in  predicting resistance to antibiotics commonly used to treat BRD compared to known ARGs.  Cultures of P. multocida were isolated from cattle with clinical signs of BRD. Antibiotic  susceptibility testing was performed and recorded for each isolate. Genomes were sequenced and  assembled, followed by annotating and identifying ARGs using the comprehensive antibiotic  resistance database (CARD). Assembled genomes were then split into 31-base long segments (31- mers), and these segments along with phenotypic antibiotic susceptibility were used as input data  for the ML algorithm. Important genomic biomarkers for four out of the six tested antibiotics were  found to have greater accuracy when predicting resistance phenotype compared to known ARGs.  The biomarker for enrofloxacin had the highest accuracy of 100% whereas the accuracy for the  12 tulathromycin biomarker was 81% but was still greater than the accuracy given by ARGs of 63%.  On the other hand, resistance genes for florfenicol and tetracycline showed greater genotype?phenotype concordance, with accuracies of 95% and 91%, respectively. Annotations to important  rulesets determined by ML were associated with clustered regularly interspaced short palindromic  repeats (CRISPR) sequences, ligases that function to recycle murein into the peptidoglycan (PDG) layer, and transferases that control the synthesis and modulation of the lipopolysaccharide (LPS).  External validation revealed that phenotypic resistance could be accurately predicted for  danofloxacin and enrofloxacin using genomic biomarkers determined by ML, and for florfenicol  using the floR gene. This study demonstrated that genomic biomarkers determined by ML can provide an accurate prediction of antibiotic resistance within Pasteurella multocida isolates.  Assays could be developed to target ML-generated biomarkers and known ARGs to predict resistance in sick animals and to limit treatment failures associated with antibiotic resistance in  cattle suffering from BRD. </p>

<p>  </p> <p><em>Fusobacterium necrophorum</em> is a Gram-negative, anaerobic, opportunistic pathogen that causes necrotic infections in cattle leading to liver abscess, foot rot, and calf diphtheria. Particularly, liver abscess in cattle is reported at 20.7% annually, and leads to liver condemnation and an annual economic burden of about 62 million dollars to the feedlot industry. Antibiotic administration is the mainstay of treating these infections, but antibiotic resistance is unavoidable and demand for antibiotic-free, natural, and organic beef has demanded alternative therapies and preventatives. Vaccination is one of the best alternatives to prophylactic antibiotic administration. In this study, we have explored outer membrane proteins (OMPs) and outer membrane vesicles (OMVs) for potential vaccine candidates. OMPs and OMVs are vaccine targets because of their antigenic properties and host specificity. Additionally, we performed comparative genomic analysis of <em>F. necrophorum</em> species to identify additional virulence genes with vaccine potential, unique to the <em>F. necrophorum</em> and its virulent subspecies <em>necrophorum</em>. </p> <p>Protein- protein interaction investigation through binding assay and pulldown assay identified novel OMPs, namely 17kDa, 22kDa, and 66.3 kDa proteins, which were further characterized as OmpH, OmpA and Cell Surface Protein (CSP), respectively. In this study, these novel OMPs including previously characterized 43kDa OMPs were cloned, and recombinant proteins were expressed and purified. These recombinant proteins were used to generate polyclonal antibodies in rabbits, and their efficacy was studied using <em>in vitro</em> adhesion inhibition assays. The combination of two or more antibodies raised against the recombinant OMPs was significantly effective in reducing/neutralizing bacterial binding to bovine endothelial cells compared to individual antibody treatment. This suggests that a multiple subunit vaccine could be effective and provide sufficient evidence to perform <em>in vivo</em> studies. </p> <p>Similarly, we purified OMVs of <em>F. necrophorum</em> subspecies <em>necrophorum</em> 8L1 and analyzed its content using proteomics and lipidomics. Out of 342 proteins identified by tandem liquid chromatography mass spectrometry (LC-MS), OMPs and toxins were the most abundant. These included OMPs and toxins namely, 43 kDa OMP, OmpH, OmpA, CSP, FadA, leukotoxin family filamentous adhesin, N-terminal domain of hemagglutinin and other OMP transport and assembly factor protein. The presence of a subset of these proteins was further confirmed by western blot analysis. Lipidomics analysis showed that OMVs contained phospholipid, sphingolipid, and acetyl carnitine as the main lipid contents. Cytotoxicity assay on BL-3 cell line showed that these OMVs have a toxic effect on host immune cells and could impart immunomodulatory effect. All these findings suggest the vaccine potential of OMVs and demand dose-based <em>in vivo</em> study.</p> <p>In addition, we identified and characterized 5 clinical isolates of <em>F. necrophorum</em> using comparative genomics, UBCG (Up-to-date Bacterial Core Gene) based analysis enabled phylogenetic characterization of 46 <em>F. necrophorum</em> genomes into subspecies specific clades. The pangenome and recombination analysis showed the extensive disparity in accessory genes resulting in species divergence. Strikingly, we detected antimicrobial resistance gene for macrolides and tetracycline in one strain of <em>F. necrophorum</em>, a harbinger of the start of resistance and necessitating search for an alternative prophylactic method. We also noted common virulence genes, including toxins, outer membrane adhesion proteins, cell envelope, type IV secretion system, ABC (ATP-binding cassette) transporters and transporter proteins in <em>F. necrophorum</em> strains. A focused study on these genes could help identify the main genes of virulence and inform effective vaccination strategies against fusobacterial infections. </p> <p>Overall, the studies suggest adhesins and toxin and/or OMV-based subunit vaccine could be potential targets for vaccine development against fusobacterial infections.  </p>