No abstract

The Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species, Elizabethkingia bruuniana and E. meningoseptica , were phenotypically tested to determine minimum inhibitory concentrations (MICs) for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies (ONT) and subsequent de novo assembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. bruuniana and 5,203 features in E. meningoseptica , where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75, and 0.54, respectively. By producing two sets of quality biological predictors, pan-genome genes and core-genome SNPs, from long-read sequence data and applying an ensemble of ML techniques, our results demonstrated that accurate phenotypic inference, at multiple AMR resolutions, can be achieved.

2The Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug 3 resistance to a range of common antibiotics. Two representative species, Elizabethkingia 4 bruuniana and Elizabethkingia meningoseptica, were phenotypically tested to determine 5 minimum inhibitory concentrations for five antibiotics. Ultra-long read sequencing with Oxford 6 Nanopore Technologies and subsequent de novo assembly produced complete, gapless circular 7 genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. 8 bruuniana and 5,203 features in E. meningoseptica, where none of these identified genes or gene 9 combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, 10 performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 11 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes 12 and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six 13 machine learning algorithms, binary classification of clindamycin and vancomycin resistance 14 achieved f1 scores of 0.94 and 0.84 respectively. Performance on the more challenging 15 multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75 16 and 0.54 respectively. 17 18 19 101Mueller-Hinton Agar (MHA) and incubating for 24 hr (37°C). The MBC was determined as the 102 lowest antimicrobial concentration in which no visual colonies were observed. 103 Library preparation 104DNA libraries were prepared separately for each Elizabethkingia isolate following the 105 procedures outlined for the SQK-LSK208 2D sequencing kit (Oxford Nanopore Technologies, 106 United Kingdom) with the following protocol adjustments. A total of 1.5 µg of gDNA was 107 sheared in g-tubes (Covaris) at 4200 RPM for a targeted fragment size of 20 kb. End-repair was 108 6 performed following the manufacturer's recommended protocol for Ultra II End-prep enzyme 109 mix (NEB). Adapter ligation reaction incubations were increased to 15 minutes. All bead clean-110 ups used 0.4x AMPureXP beads (Beckman Coulter, Brea, CA) for additional size selection and 111 elutions were performed at 37°C for 20 minutes. DNA concentration of the library was 112 quantified using Quant-IT PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific), measured 113 on Synergy H1, hybrid multi-mode microplate reader (BioTek). Final DNA library yields were 114 above the recommended 200 ng. 115 Single molecular real time sequencing 116 Two R9.4 flow cells were prepared for two corresponding MinIONs, each connected to a 117 separate Windows PC using a USB 3.0 connection. MinKNOW GUI application 1.0.8.0 from 118 Oxford Nanopore Technologies (ONT) was used to validate the MinION connection and to 119 monitor basic hardware details, like the number of active pores within each flow cell during 120 sequencing runs. Pore count validation was completed beforehand, with the Platform QC 121 command in MinKNOW. Flow cell prim...

35Disruptive innovations in long-range, cost-effective direct template nucleic acid sequencing are 36 transforming clinical and diagnostic medicine. A multidrug resistant strain and a pan-susceptible 37 strain of Mannheimia haemolytica, isolated from pneumonic bovine lung samples, were 38 respectively sequenced at 146x and 111x coverage with Oxford Nanopore Technologies 39 MinION. De novo assembly produced a complete genome for the non-resistant strain and a 40 nearly complete assembly for the drug resistant strain. Functional annotation using RAST (Rapid 41 Annotations using Subsystems Technology), CARD (Comprehensive Antibiotic Resistance 42 Database) and ResFinder databases identified genes conferring resistance to different classes of 43 antibiotics including beta lactams, tetracyclines, lincosamides, phenicols, aminoglycosides, 44 sulfonamides and macrolides. Antibiotic resistance phenotypes of the M. haemolytica strains 45 were confirmed with minimum inhibitory concentration (MIC) assays. The sequencing capacity 46 of highly portable MinION devices was verified by sub-sampling sequencing reads; potential for 47 antimicrobial resistance determined by identification of resistance genes in the draft assemblies 48 with as little as 5,437 MinION reads corresponded to all classes of MIC assays. The resulting 49 quality assemblies and AMR gene annotation highlight efficiency of ultra long-read, whole-50 genome sequencing (WGS) as a valuable tool in diagnostic veterinary medicine.51 52 53 54 55 56Emergence of antimicrobial resistance (AMR) among the most important bacterial pathogens is 57 recognized as a major public health concern. Not only has AMR emerged in hospital 58 environments, it is often identified in community settings, in livestock feedlots and in 59 aquaculture and crop production, suggesting an ever-increasing range of reservoirs of antibiotic-60 resistant bacteria (1-4). Bacterial response to the antibiotic "attack" is the prime example of 61 genetic adaptation through the interplay of immense genetic plasticity, ranging from mutational 62 adaptations and acquisition of genetic material to alteration of gene expression with fitness 63 consequence of the pathogen (5). As a result, understanding the genetic basis of resistance is of 64 paramount importance to design strategies to curtail the emergence and spread of AMR, as well 65 as to devise innovative therapeutic approaches against multidrug-resistant organisms (6). 66With an increased population of pet and farm animals there is a significant risk of 67 humans acquiring drug resistant bacteria from animal sources. One of the major deficiencies in 68 veterinary medicine is the lack of validated data to determine minimum inhibitory concentration 69 (MIC) breakpoints for drug-microbe-host combinations, based on which scientifically sound 70 interpretations can be made regarding whether a pathogen is susceptible or resistant to a specific 71 drug. This is especially true for anaerobic bacteria. The large diversity of domestic and exotic 72 animal sp...