From Investigating a Case of Cellulitis to Exploring Nosocomial Infection Control of ST1 Legionella pneumophila Using Genomic Approaches

Abstract

Legionella pneumophila can cause a large panel of symptoms besides the classic pneumonia presentation. Here we present a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of Legionnaires' disease in the same ward. While the first case was easily assumed as nosocomial based on the date of symptom onset, the second case required clear typing results to be assigned either as nosocomial and related to the same environmental source as the first case, or community acquired. To untangle this specific question, we applied core-genome multilocus typing (MLST), whole-genome single nucleotide polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large-scale epidemiological investigations, next-generation sequencing typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of the L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in the environment.

Keywords: Legionella pneumophila; Legionella pneumophila ST1; cgMLST; genomic typing; hygiene investigation; nosocomial; wgMLST; wgSNP; whole genome sequencing.

Figure 1

Minimum spanning tree for categorical data of the cgMLST analysis based on the call of 1521 alleles [12] performed using Bionumerics software v8.1 on nine isolates from Hospital 1, including environmental isolates related to Patients 1 and 2. A cluster analysis of maximum 4 allelic differences between two isolates is highlighted in red. Branch lengths are logarithmically scaled and allelic distances are written on branches.

Figure 2

Minimum spanning tree performed on Bionumerics software v8.1. ST1 isolates of L. pneumophila (n = 77) are represented by nodes colored according to their country of origin. (A): cgMLST. (B): wgSNP with strict SNP filtering. (C): wgMLST (5770 alleles). Branch lengths are logarithmically scaled and allelic distances are written on branches.

Figure 3

Minimum spanning tree for categorical data of the cgMLST analysis based on the call of 1521 alleles [12] performed using Bionumerics software v8.1 on a panel of 77 ST1 isolates. L. pneumophila Paris 1 was used as a reference. Nodes representing non-Belgian isolates were reduced to uncover the relationship between the 36 Belgian ST1 isolates. The setting of each isolate is written above each node: Hospital 1,2, 3 and 4: H1, H2, H3 and H4, respectively; sporadic cases: S; Patient 1 and 2: P1 and P2, respectively; environmentally linked isolates: E. Clustering performed using a distance of four allelic differences (ADs) between isolates appears against a colored background. The solid blue line corresponds to the threshold of six ADs proposed by authors to include well characterized epidemics in H2 and H3. The cluster obtained for H1 isolates using these six ADs is circled in a blue dashed line, and includes one H3 isolate and the two H4 isolates. Branch lengths are logarithmically scaled and allelic distances are written on branches.

Figure 4

Minimum spanning tree of wgSNP with strict SNP filtering performed by mapping the reference strain L. pneumophila Paris 1 on Bionumerics software v8.1. Nodes representing non-Belgian isolates were reduced to uncover the relationship between the 36 Belgian ST1 isolates. The setting of each isolate is written above each node: Hospital 1,2, 3 and 4: H1, H2, H3 and H4, respectively; sporadic cases: S; Patient 1 and 2: P1 and P2, respectively; environmentally linked isolates: E. Clustering performed using a four allelic differences (ADs) distance between isolates appears against a colored background. The solid blue line corresponds to the threshold of eight SNPs proposed to include well characterized epidemics in H2 and H3. The cluster obtained for H1 isolates using these eight SNPs is circled in a blue dashed line, entails one H3 isolate and the two H4 isolates, but excludes the P2 clinical isolate. Branch lengths are logarithmically scaled and allelic distances are written on branches.

Figure 5

Minimum spanning tree of categorical data analysis for wgMLST performed on Bionumerics software v8.1. Nodes representing non-Belgian isolates were reduced to undercover only the relationship between the 36 Belgian ST1. The setting of each isolate is written above each node. Hospital 1,2, 3 and 4: H1, H2, H3 and H4, respectively; sporadic cases: S; Patient 1 and 2: P1 and P2, respectively; environmentally linked isolates: E. Clustering was performed using a distance of 12 allelic differences between isolates and appears against a colored background within the groups. Solid blue line shows a well-defined cluster formed for H2 and H3. Dashed blue line shows that the suspected cluster excludes the P2 clinical isolate with the chosen threshold. Branch lengths are logarithmically scaled and allelic distances are written on branches.