Development of a Rapid, Efficient, Intelligent and Cost-Saving Tool to Diagnose Pasteurella Multocida by Using Whole Genome Sequence and Genotypes of Pasteurella Multocida From Different Hosts

Background: Different typing systems including capsular genotyping, lipopolysaccharide (LPS) genotyping, multilocus sequence typing (MLST), and virulence genotyping based on the detection of different virulence factor-encoding gene (VFG) profiles have been applied to characterize Pasteurella multocida strains from different host species. However, these methods require much time and effort in laboratories. Particularly, relying on one of these methods is difficult to address the biology of P. multocida from host species. Recently, we found that assigning P. multocida strains according to the combination of their capsular, LPS, and MLST genotypes (marked as capsular genotype: LPS genotype: MLST genotype) could help address the biological characteristics of P. multocida circulation in multiple hosts. However, it is still lack of a rapid, efficient, intelligent and cost-saving tool to diagnose P. multocida according to this system. Results: We have developed an intelligent genotyping and host tropism prediction tool PmGT for P. multocida strains according to their whole genome sequences by using machine learning and web 2.0 technologies. By using this tool, the capsular genotypes, LPS genotypes, and MLST genotypes as well as the main VFGs of P. multocida isolates in different host species were determined based on whole genome sequences. The results revealed a closer association between the genotypes and pasteurellosis rather than between genotypes and host species. Finally, we also used PmGT to predict the host species of P. multocida strains with the same capsular: lipopolysaccharide: MLST genotypes. Conclusions: With the advent of high-quality, inexpensive DNA sequencing, this platform represents a more efficient and cost-saving tool for P. multocida diagnosis in both epidemiological studies and clinical settings.

A16 Next-generation sequencing to detect transmitted drug resistance mutations in Romanian people who inject drugs

Romania has faced an HIV outbreak among people who inject drugs (PWID) since 2011. The introduction of so-called ‘legal highs’ (amphetamine-type stimulants) on the drug market a few years prior contributed substantially to this outbreak. Next-generation sequencing (NGS) provides the possibility to detect drug resistance mutations with higher sensitivity than Sanger sequencing. The aim of this study was to search for transmitted drug resistance (TDR) mutations in strains from PWID recently diagnosed with HIV infection by parallel use of Sanger sequencing and NGS. The study was conducted on strains from 34 PWID diagnosed with HIV infection between 2016 and 2017. Sequencing was performed for the pol (PR, RT, and INT) and env (V2-V3 loop) regions. Sanger sequencing was performed with the commercial ViroseqTMHIV-1 Genotyping system (Abbott Laboratories) and with an in-house protocol for the env gene. NGS was performed in the same genomic regions using Nextera DNA Library Preparation Kit (Illumina) and the Miseq instrument (Illumina). NGS data were processed for error correction, read mapping, and detection of drug resistance mutations with HIV-1 Deepchek analysis software. Geno2pheno algorithm was used for viral tropism prediction and the WHO 2009 list for TDRM analysis. By using NGS, we detected seven cases (20.6%) of TDR in PWID and only two cases (5.8%) with standard sequencing. The TDR mutations detected by NGS were K103N, K101EN, Y181C, T215S in RT gene, I54V and M46L in PR, and none in INT. Two NNRTI mutations (K103N and K101EN) were detected in the same sample. Most of the TDR identified were present in the minority population (between 1% and 2% of the total reads) explaining the higher sensitivity of NGS method compared with standard sequencing. No significant differences were observed between these two methods when tropism prediction was analyzed. The majority of the viruses circulating in this group were R5-tropic. All strains showed more resistance mutations when analyzed by deep sequencing than by Sanger sequencing and more than previously observed in other risk groups. NGS proved to be a sensitive tool to detect TDR in newly infected PWID.

Coreceptor Tropism and Maraviroc Sensitivity of Clonally Derived Ethiopian HIV-1C Strains Using an in-house Phenotypic Assay and Commonly Used Genotypic Methods

Objectives:Genotypic Tropism Testing (GTT) tools are generally developed based on HIV-1 subtype B (HIV-1B) and used for HIV-1C as well but with a large discordance of prediction between different methods. We used an established phenotypic assay for comparison with GTT methods and for the determination of in vitro maraviroc sensitivity of pure R5-tropic and dual-tropic HIV-1C.Methods:Plasma was obtained from 58 HIV-1C infected Ethiopians. Envgp120 was cloned into a luciferase tagged NL4-3 plasmid. Phenotypic tropism was determined by in house method and the V3 sequences were analysed by five GTT methods. In vitro maraviroc sensitivity of R5-tropic and dual-tropic isolates were compared in the TZMbl cell-line.Results:The phenotypes were classified as R5 in 92.4% and dual tropic (R5X4) in 7.6% of 79 clones. The concordance between phenotype and genotype ranged from 64.7% to 84.3% depending on the GTT method. Only 46.9% of the R5 phenotypes were predicted as R5 by all GTT tools while R5X4 phenotypes were predicted as X4 by four methods, but not by Raymond’s method. All six tested phenotypic R5 clones, as well as five of six of dual tropic clones, showed a dose response to maraviroc.Conclusion:There is a high discordance between GTT methods, which underestimates the presence of R5 and overestimates X4 strains compared to a phenotypic assay. Currently available GTT algorithms should be further improved for tropism prediction in HIV-1C. Maraviroc has an in vitro activity against most HIV-1C viruses and could be considered as an alternative regimen in individuals infected with CCR5-tropic HIV-1C viruses.

X4 Tropic Virus Prediction Is Associated with a Nadir CD4 T-Cell Count below 100 Cells/mm3

Objective: The aim of this study was to evaluate tropism prediction performances of three algorithms [geno2pheno false-positive rate 10% (G2P10), position-specific scoring matrix (PSSM) and a combination of the 11/25 and net charge rules] and to investigate the viral and host factors potentially involved in the X4 or R5 prediction in human immunodeficiency virus-1 (HIV-1) patients. Methods: Viral tropism was determined in 179 HIV-1-infected patients eligible for CCR5 antagonist therapy. HIV-1 RNA or DNA was extracted and amplified for env gp120 sequencing. In parallel, demographic, viral, immunological and clinical determinants were analyzed. Results: According to the G2P10 algorithm, 48 patients harbored X4 or X4R5 virus. The tropism prediction was concordant for 87.7 and 88.2% of samples when comparing G2P10 with PSSM or with a combination of the 11/25 and net charge rules, respectively. X4 prediction was significantly associated with more than 35 amino acids in the V3 domain (p < 0.0001) and loss of an N-linked glycosylation site (p < 0.0001). Of the factors studied, only the nadir CD4 T-cell count was significantly associated with X4 tropism (p = 0.01). Conclusion: We determined that the X4 virus detection is closely linked to the nadir CD4 T-cell count below 100 cells/mm3 that must be taken into account when considering a CCR5 antagonist therapy switch.

Performance of Genotypic Tools for Prediction of Tropism in HIV-1 Subtype C V3 Loop Sequences

Currently, there is no consensus on the genotypic tools to be used for tropism analysis in HIV-1 subtype C strains. Thus, the aim of the study was to evaluate the performance of the different V3 loop-based genotypic algorithms available. We compiled a dataset of 645 HIV-1 subtype C V3 loop sequences of known coreceptor phenotypes (531 R5-tropic/non-syncytium-inducing and 114 X4-tropic/R5X4-tropic/syncytium-inducing sequences) from the Los Alamos database (http://www.hiv.lanl.gov/) and previously published literature. Coreceptor usage was predicted based on this dataset using different software-based machine-learning algorithms as well as simple classical rules. All the sophisticated machine-learning methods showed a good concordance of above 85%. Geno2Pheno (false-positive rate cutoff of 5-15%) and CoRSeqV3-C were found to have a high predicting capability in determining both HIV-1 subtype C X4-tropic and R5-tropic strains. The current sophisticated genotypic tropism tools based on V3 loop perform well for tropism prediction in HIV-1 subtype C strains and can be used in clinical settings.