Genome dynamics and evolution of Salmonella Typhi strains from the typhoid-endemic zones

Abstract

Typhoid fever poses significant burden on healthcare systems in Southeast Asia and other endemic countries. Several epidemiological and genomic studies have attributed pseudogenisation to be the major driving force for the evolution of Salmonella Typhi although its real potential remains elusive. In the present study, we analyzed genomes of S. Typhi from different parts of Southeast Asia and Oceania, comprising of isolates from outbreak, sporadic and carrier cases. The genomes showed high genetic relatedness with limited opportunity for gene acquisition as evident from pan-genome structure. Given that pseudogenisation is an active process in S. Typhi, we further investigated core and pan-genome profiles of functional and pseudogenes separately. We observed a decline in core functional gene content and a significant increase in accessory pseudogene content. Upon functional classification, genes encoding metabolic functions formed a major constituent of pseudogenes as well as core functional gene clusters with SNPs. Further, an in-depth analysis of accessory pseudogene content revealed the existence of heterogeneous complements of functional and pseudogenes among the strains. In addition, these polymorphic genes were also enriched in metabolism related functions. Thus, the study highlights the existence of heterogeneous strains in a population with varying metabolic potential and that S. Typhi possibly resorts to metabolic fine tuning for its adaptation.

Figure 1. Phylogenomic Tree.

The whole genome information was used to build the distance matrix using Gegenees. The phylogenetic tree was developed using SplitsTree by NJ method. This revealed close similarity among genomes and also co-clustering of strains isolated from the same regions.

Figure 2. Genome alignment.

The whole genome alignment of all eight genomes was generated using progressiveMauve. Each colored block represents similar sequences in the respective genomes.

Figure 3. Pan and Core Genome Distribution.

(a). Pan and core genome developments using median values of the combinations of all eight genomes. (b). Pan and core genome developments of functional genes of these eight isolates. Here it can be observed that core genome is decreasing sharply. (c). Pan and core genome developments of pseudogenes of these eight isolates. It can be seen that pan genome of pseudogenes is highly non conservative with a steep increase in accessory content while the core genome reached convergence.

Figure 4. The proportion of functionally classified pseudogenes and the functional genes with SNPs according to COG classification.

The pie chart represents the proportion of various functional classes among the pseudogenes and the functional genes with SNPs. The figure clearly shows the enrichment of metabolism related genes in pseudogenes and the functional genes with SNPs.

Figure 5. Accessory pseudogene clusters analysis.

The status of genes in each accessory pseudogene cluster was marked as P for pseudogene, F for functional complement and N for absence of gene. The clusters where the orthologs were present in P or F states were considered in plot. This shows the heterogeneous existence of functional and pseudogene complements in the population.

Figure 6. Proportion of heterogeneous genes classified according to COG functional categories.

The figure represents the distribution of accessory pseudogenes (those having variable functional and pseudogene status in atleast two strains) among various COG functional categories. The genes related to metabolism were clearly enriched in the accessory pseudogenes.