A model system for studying the transcriptomic and physiological changes associated with mammalian host-adaptation by Leptospira interrogans serovar Copenhageni

Abstract

Leptospirosis, an emerging zoonotic disease with worldwide distribution, is caused by spirochetes belonging to the genus Leptospira. More than 500,000 cases of severe leptospirosis are reported annually, with >10% of these being fatal. Leptospires can survive for weeks in suitably moist conditions before encountering a new host. Reservoir hosts, typically rodents, exhibit little to no signs of disease but shed large numbers of organisms in their urine. Transmission occurs when mucosal surfaces or abraded skin come into contact with infected urine or urine-contaminated water or soil. In humans, leptospires can cause a variety of clinical manifestations, ranging from asymptomatic or mild fever to severe icteric (Weil's) disease and pulmonary haemorrhage. Currently, little is known about how Leptospira persist within a reservoir host. Prior in vitro studies have suggested that leptospires alter their transcriptomic and proteomic profiles in response to environmental signals encountered during mammalian infection. However, no study has examined gene expression by leptospires within a mammalian host-adapted state. To obtain a more faithful representation of how leptospires respond to host-derived signals, we used RNA-Seq to compare the transcriptome of L. interrogans cultivated within dialysis membrane chambers (DMCs) implanted into the peritoneal cavities of rats with that of organisms grown in vitro. In addition to determining the relative expression levels of "core" housekeeping genes under both growth conditions, we identified 166 genes that are differentially-expressed by L. interrogans in vivo. Our analyses highlight physiological aspects of host adaptation by leptospires relating to heme uptake and utilization. We also identified 11 novel non-coding transcripts that are candidate small regulatory RNAs. The DMC model provides a facile system for studying the transcriptional and antigenic changes associated with mammalian host-adaptation, selection of targets for mutagenesis, and the identification of previously unrecognized virulence determinants.

Figure 1. Virulent leptospires become mammalian host-adapted during growth within dialysis membrane chambers.

Representative whole cell lysates of leptospires cultivated to late-logarithmic phase in EMJH medium at 30°C in vitro (IV) and within dialysis membrane chambers (DMC) implanted into the peritoneal cavities of female Sprague-Dawley rats. (A) Lysates were loaded according to the numbers of leptospires (5×10⁶ per lane) or total protein (5 µg per lane) and stained with SYPRO Ruby gel stain. Arrows and asterisks are used to highlight examples of polypeptides whose expression appears to be increased or decreased, respectively, within DMCs compared to in vitro. Molecular mass markers are indicated on the left. (B) Immunoblot analyses using rabbit polyclonal antisera directed against Sph2 , LipL32 and LipL41 . An arrow is used to indicate a band of the predicted molecular mass for SphH, a second, closely-related sphingomyelinase in L. interrogans recognized by antiserum directed against Sph2 , .

Figure 2. Mapping of RNA-Seq reads.

Percentage of uniquely mapping reads from each biological replicate of leptospires cultivated in DMCs or under standard in vitro growth conditions (30°C in EMJH).

Figure 3. Conservation of L. interrogans sv. Copenhageni Fiocruz L1-130 differentially-expressed genes among virulent and saprophytic Leptospira spp. Protein sequence similarities were determined using GLSEARCH (v. 34.05).

Genomes used for analysis: L. interrogans sv. Lai strain 56601, L. borgpetersenii sv. Hardjo strain L550, L. santarosai sv. Shermani strain LT821; L. licerasiae sv. Varillal strain VAR010; and L. biflexa sv. Patoc strain Patoc1 Ames, respectively. The color coding used in the heat map is as follows: blue, 95–100% identity; green, 90–94% identity; orange, 85–89%; and yellow, 80–84%.

Figure 4. Functional categories of genes differentially-expressed by L. interrogans sv Copenhageni strain Fiocruz L1-130 within DMCs.

Functional categories are based on those of , and the Leptospira interrogans sv. Copenhageni Genome Project database (http://aeg.lbi.ic.unicamp.br/world/lic/). The number of upregulated (Ups) and downregulated (Down) genes within each category are indicated in red and blue, respectively.

Figure 5. IGB viewer of normalized gene expression data for the flagellar genes fliE, flgB and flgC.

Visualization of normalized mapped reads for minus (-) strand of an operon encoding genes fliE, flgB and flgC of the flagellar proximal rod shows increased expression by leptospires cultivated in dialysis membrane chambers (DMC, green) compared to those cultivated in vitro (IV, red). Annotated genes on Chromosome 1 are in blue. The vertical “read count” scale is 0–50.

Figure 6. IGB viewer of candidate sRNA LIC1nc80.

LICnc80 was identified as an area of high transcriptional activity within an intergenic region of the genome of L. interrogans sv. Copenhageni Fiocruz L1-130. Expression data for leptospires cultivated in DMCs (green) compared to those cultivated in vitro (IV, red) are indicated on the plus strand of the genome. Annotated genes on the relevant chromosome and nucleotide co-ordinates are indicated. The vertical “read count” scale is 0–100.

Figure 7. Validation of comparative RNA-Seq analysis.

(A) qRT-pCR analysis of representative genes identified by RNA-Seq. Values represent the average transcript copy numbers for each gene normalized per lipL32 transcript. Bars indicate the standard error of the mean (SEM). Results presented are mean values from at least 3 biologically-independent samples of leptospires for each growth condition. The fold-regulation for each gene determined by RNA-Seq is indicated in parentheses. The fold-regulation between in vitro- (IV) and DMC-cultivated leptospires determined by qRT-PCR are indicated. P values were calculated using an unpaired t-test. (B) Correlation coefficient (R²) between RNA-Seq and qRT-PCR data.