Transcriptome analysis of Neisseria meningitidis in human whole blood and mutagenesis studies identify virulence factors involved in blood survival

Abstract

During infection Neisseria meningitidis (Nm) encounters multiple environments within the host, which makes rapid adaptation a crucial factor for meningococcal survival. Despite the importance of invasion into the bloodstream in the meningococcal disease process, little is known about how Nm adapts to permit survival and growth in blood. To address this, we performed a time-course transcriptome analysis using an ex vivo model of human whole blood infection. We observed that Nm alters the expression of ≈30% of ORFs of the genome and major dynamic changes were observed in the expression of transcriptional regulators, transport and binding proteins, energy metabolism, and surface-exposed virulence factors. In particular, we found that the gene encoding the regulator Fur, as well as all genes encoding iron uptake systems, were significantly up-regulated. Analysis of regulated genes encoding for surface-exposed proteins involved in Nm pathogenesis allowed us to better understand mechanisms used to circumvent host defenses. During blood infection, Nm activates genes encoding for the factor H binding proteins, fHbp and NspA, genes encoding for detoxifying enzymes such as SodC, Kat and AniA, as well as several less characterized surface-exposed proteins that might have a role in blood survival. Through mutagenesis studies of a subset of up-regulated genes we were able to identify new proteins important for survival in human blood and also to identify additional roles of previously known virulence factors in aiding survival in blood. Nm mutant strains lacking the genes encoding the hypothetical protein NMB1483 and the surface-exposed proteins NalP, Mip and NspA, the Fur regulator, the transferrin binding protein TbpB, and the L-lactate permease LctP were sensitive to killing by human blood. This increased knowledge of how Nm responds to adaptation in blood could also be helpful to develop diagnostic and therapeutic strategies to control the devastating disease cause by this microorganism.

Figure 1. Growth of Nm in human whole blood and RNA analysis.

(A) Number of bacteria during incubation with human blood. The CFU/ml per single donor is shown during a time course experiment. (B) Analysis of isolated total RNA and enriched Nm RNA using a BioAnalyzer 2100 (Agilent). Upper panel: Total RNA collected from Nm incubated in human whole blood, bacterial RNA (shaded arrowheads) and eukaryotic RNA (open arrowheads) are indicated. Lower panel: Enriched bacterial RNA.

Figure 2. Global changes of Nm gene expression in human whole blood.

(A) Experimental design. Human blood isolated from four different donors was incubated with Nm and RNA extracted at the indicated time points (samples from each time point was done in triplicate and then pooled). Time 0 was used as the reference time point. (B) Hierarchical clustering of the differentially expressed genes showing the data of the four different donors (Donors 1–4) and the average dataset (Merge). Clustering showed two well defined partitions of the expression profiles, 360 up-regulated (red) and 277 down-regulated genes (green). Genes were selected based on a fold change of at least two (log₂ ratio <−1 or >1) and a t-test p-value <0.05. (C) Clusters of differentially expressed genes defined by the K-means algorithm and grouped based on the dynamics of expression changes during the time course (black lines) and mean expression values of genes located in defined clusters (red lines). The number of genes included within each cluster is reported in blue between brackets. TIGRFAM main roles and KEGG pathways that significantly correlated with clusters are reported in Table S2.

Figure 3. Time course distribution of up- and down- regulated genes within TIGRFAM main roles.

The plot reflects the dynamics of Nm metabolic adaptation to blood, and the number of regulated genes within each TIGR family is shown for each time point. The total number of genes in each class and the number of up- and down-regulated genes are listed in the table.

Figure 4. Transcriptional profile of differentially regulated genes grouped by functional TIGRFAM family main roles.

Detailed expression profiles of functionally related genes during the time course of Nm in human whole blood. Clusters were created using TMEV. (A) Regulatory functions (B) Transport and binding proteins (C) Energy metabolism (D) Amino acid biosynthesis. Each gene is represented by a single row and each time point by a single column; gene identification numbers (based on the MC58 annotation) and gene definitions are reported on the right. Gene expression is displayed in fold change represented by the color bar under the figure. The numerical gene expression values are shown for all the genes at the different time points. For a more detailed analysis, see Table S1.

Figure 5. Transcriptional profile of Nm genes coding for proteins involved in host-pathogen interaction.

Detailed expression profile of functional genes coding for proteins that play a role in adhesion, survival and other functions. Gene identification numbers (based on the MC58 annotation) and gene names are reported on the right. Genes that were calculated as being significantly regulated are underlined. The numerical gene expression values are shown for all the genes at the different time points.

Figure 6. Survival of MC58 and 95N477 wild-type and deletion mutant strains in the ex vivo whole blood model of meningococcal septicemia.

Deletion mutants in the genetic backgrounds MC58 (panel A) and 95N477 (panel B) of the selected up-regulated genes were tested for survival using the ex vivo whole human blood model over a time course of 120 minutes. The phenotype of the specific mutants were compared to the wild-type strain. MCΔfHbp deletion mutant was used as a control (A1). Deletion mutants with a significant sensitivity to killing by human blood with respect to MC58 wild-type are reported in panels A2-7, while those that were not significantly sensitive are reported in panels A8-9. Deletion mutants with a phenotype in 95N477 genetic background are reported in panels B2-5, while those that were not significantly sensitive to whole human blood are reported in panel B6. Survival of the fHbp deletion mutant in 95N477 is reported in panel B1. The insets of each panel represent the growth control in GC medium for the same time course of incubation as performed in whole blood.

Figure 7. Survival of MC58 and 95N477 complementing strains in the ex vivo whole blood model of meningococcal septicemia.

Results show the survival of the wild-type, deletion mutant and complementing strains in human whole blood for fur (panel 1), mip (panel 2) and NMB1483 (panel 3) in MC58 and nspA (panel 4) in 95N477. The insets of each panel represent the growth control in GC medium for the same time course of incubation as performed in whole blood.

Figure 8. Schematic overview of the factors involved in adaptation and survival of Nm in human blood.

The model presents the transcriptional response of Nm genes according to their functional classes (TIGRFAM main roles: energy metabolism, amino acid biosynthesis, transport and binding proteins and regulatory functions) and genes coding for factors involved in host-pathogen interactions (adhesins, survival). The pathways and specific genes mentioned in the results and discussion section are reported. Colors indicate up-regulated genes (red), down-regulated genes (green) and not differentially regulated genes (grey).