Virulence and AMR Detection

Finding methods

Disease surveillance has as its ultimate goal the decrease of human (and animal) illness. This is done not only by the rapid detection and control of disease outbreaks, for which WGS typing methods represent a relevant technological advance (see Serotyping section), but also by the identification of phenotypically-relevant markers (and their changes at the population level), such as virulence- or antimicrobial resistance-associated genes. Therefore, the analysis of the virulome (complete set of virulence genes) and the resistome (complete set of antimicrobial resistance genes) is of extreme relevance in the context of surveillance and outbreak control.

Differences at the genome level involving point mutations or presence/absence of certain loci may have great impact on a pathogen’s behavior, and consequently on the disease. For example, presence of tetO and point mutations in gyrA have been associated with increased resistance to tetracyclines and fluoroquinolones in Campylobacter jejuni (Fiedoruk et al. 2019). Moreover, specific genes, such as those involved in the adhesion to human cells or those related to the efflux of certain molecules, have been particularly associated in many microorganisms with virulence and antimicrobial resistance, respectively (Poimenidou et al. 2018, Anbazhagan et al. 2019, Vieira et al. 2017). These genomic features can be acquired not only by vertical evolution, but also by horizontal gene transfer. For instance, the existence of plasmids and transposable elements allows the interchange of genetic material between distantly related lineages (reviewed in Gyles and Boerlin, 2014), thus contributing to the introduction and expansion of new virulence- or resistance-related phenotypes in some lineages. This may contribute to the emergence of different phenotypes which may impact, for example, the pathogenic behavior and the host range. Therefore, there is a need for constant surveillance of the resistome and the virulome in bacterial pathogens.

Similarly to what occurs with molecular typing, virulome and resistome analysis relies on the assessment of the presence of genetic traits (specific genes or mutations) which have previously been associated with relevant phenotypes. In the pre-WGS era, this search could be performed, for example, by amplification and detection of specific target genes. Such an approach could be particularly challenging when encountering unexpected genomic changes which could prevent the amplification of the region of interest, or by the presence of horizontally transferred genes which would not be detected. By providing information at the whole-genome level, WGS can bypass these issues as information is expected to be provided independently of the genetic variability of the sample. With WGS data, the identification of genes/alleles of particular interest can be performed by comparison of the genome of the sample to a database comprising precisely the set of genes of interest. In the particular case of the virulome and the resistome, there are public databases where those sets of genes are already available and programs which automatically perform this search.

Database resources

Several databases exist for both antimicrobial resistance-associated genes and mutations and virulence genes. These differ in their content and curation procedures, and may therefore produce different outputs when used within the same tool. Some databases have species-specific subdatasets, such as the PointFinder and VirulenceFinder databases. Other databases have more comprehensive content, such as MEGARes, CARD, and VFDB. A user should be cautious when selecting a database, and have knowledge about their limitations and content, as it is only possible to identify the genes/mutations that are present in the database. Examples of predefined resistome databases for bacteria include:

Examples of predefined virulome databases for bacteria include:

Tools

ResFinder, PathogenFinder and VirulenceFinder are associated with web sites that have the same name as the database where searches for genes of interest can be done. ResFinder has the option to search for both acquired resistance genes, as well as resistance-associated chromosomal mutations. The above tools also exist as command-line tools, which can be implemented into workflows and pipelines. While these web- or command line- based programs rely on their respective databases, other programs have been developed to perform a broader search for the genes of interest by integrating several of the above-mentioned databases. Examples of such programs are

Moreover, ABRicate and ARIBA allows the user to create their own databases. database.

The programs mentioned above generally work in one of two modes, either via a sequence search using BLAST where the assembled genome is compared to a database (ABRicate, AMRFinderPlus, ResFinder), or via mapping reads to a database (ARIBA). In both cases the results from the search or from the mapping is evaluated and interpretation leads to conclusions regarding virulence/resistance. Exceptions to this are PathogenFinder, which applies prediction models to determine the pathogenic nature of the isolate, and the most recent versions of ResFinder and PointFinder, which have incorporated KMA allowing the k-mer based alignment of genomic reads (no need for genome assembly) on the database.

Interpretation of results

Regardless of the tool, the main output provided by this kind of analysis is the presence/absence of gene of interest or mutations (i.e., genotype) and their potential impact on the phenotype (e.g., increased virulence, antibiotic resistance). This output is usually provided in tabular format (e.g., text files, which are useful for automated report generation and downstream applications), in combination with additional output files for better data visualization and interpretation (e.g., interactive QC color codes, graphics, etc). This is true for both the online and command-line versions of the tools, where the command-line version often has the option to produce additional output files. The tools can thus provide a lot of information, and it is important that the user spends time on understanding the results to ensure that there is no mixup with regards to what the results mean. For example, ARIBA may produce reports that scale several hundred rows of data for a single isolate. This is due to the extensive quality control of each gene and/or variant identified, where ARIBA supplies a “flag” to describe the success or failure of the process. Each flag has its own interpretation, which the user needs to be aware of to interpret the results correctly. Reports of such scale are therefore not meant for in-depth human reading, but rather for automatic handling and interpretation by set rules. This is already supplied with ARIBA, as it can interpret and summarise the results from several isolates with one line of code. The detailed output also allows the user to find and implement their own rules that can satisfy the needs for their situation.

Due to the differences in output reports, comparing results across tools may be a difficult task. The hAMRonization tool addresses the issue of comparing results from several AMR gene-finding tools. hAMRonization is a parser tool that combines the output of several AMR gene-finding tools and generates a standardised AMR gene report, easing interpretation and comparison of tools.