In vivo dual RNA-Seq uncovers key effectors of epithelial barrier disruption by an extracellular pathogen

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
Disruption of host cell barriers is a fundamental strategy enabling pathogens to establish a paracellular infection. Here, using dual RNA-Seq, we determine the in vivo host-pathogen transcriptomic landscape upon infection by the extracellular pathogen Leptospira interrogans and uncover a mechanism of cell–cell junction disruption. We demonstrate that, upon infection, an increase in intracellular calcium triggers tight junction destabilization, by activating the calmodulin and myosin light chain kinase signalization. We identify two bacterial effectors of the Virulence-Modifying (VM) proteins family, structurally related to toxin-like proteins, that promote modulation of calcium homeostasis and disruption of cell–cell junctions, thereby allowing Leptospira translocation across epithelium barriers, tissue colonization and pathogenicity. Furthermore, we demonstrate that at least one of these VM proteins is secreted and associates with host cells. Altogether, these findings reveal a unique strategy by which an extracellular pathogen secretes toxin-like proteins to exploit host calcium signaling for breaching epithelial barriers.

Introduction
Successful microbial infection and host colonization depend on the ability of pathogens to evade, resist and/or manipulate host defenses. While much of our understanding of mechanisms underlying the dynamic interplay between a host and a pathogen (i.e., host-pathogen interactions) stems from studies on well-characterized intracellular and extracellular pathogens, our understanding remains incomplete for many extracellular species that lack canonical virulence factors. Moreover, these interactions have been generally investigated under in vitro condition that cannot fully replicate the complexity of a living host.

A critical feature to establish infection is the ability of extracellular pathogens to compromise the integrity of endothelial and epithelial barriers1, which are vital for maintaining organ integrity. Tissue integrity depends on protein scaffolds stabilizing cell–cell junctions, including tight junctions (TJs) and adherens junctions (AJs). TJs are formed by claudin, occludin, JAM-1 and zonula occludens (ZO) proteins while AJs are constituted of the calcium-binding protein cadherin2. Both TJs and AJs interact with the actin cytoskeleton to maintain barrier integrity. Pathogens often disrupt these junctions, directly or indirectly, through cytoskeletal regulation to invade a host3. Although several bacterial toxins and effectors from well-studied extracellular pathogens have been shown to disrupt cell–cell junctions, the detailed molecular mechanisms underlying junction disruption remain poorly understood for a variety of bacteria.

Among extracellular pathogens, Leptospira spp., the causative agents of leptospirosis, have evolved strategies to breach these barriers and disseminate throughout the host. These bacteria enter through skin abrasion or mucosa, evade the innate immune response and rapidly disseminate hematogenously to organs, including kidneys and liver, resulting in multiple organ failure and hemorrhage. With an estimated one million severe cases and 60,000 deaths reported annually4, leptospirosis is the most widespread zoonotic disease. Previous studies have demonstrated that L. interrogans infect their mammalian host through a paracellular route, disrupting both TJs and AJs5,6. Because Leptospira exclusively employs a paracellular route to colonize host tissues, it served as an ideal model to understand mechanisms of cell–cell junction disruption by extracellular pathogens.

Here, we developed a dedicated protocol for dual RNA-Seq profiling of L. interrogans-infected hamsters, a model of human acute leptospirosis, to elucidate how this extracellular pathogen spreads and compromises cell barrier integrity. Transcriptional host response was characterized by alterations in pathways associated with paracellular permeability, including cell–cell junctions and cytoskeletal organization. We demonstrated that the disruption of cell–cell junctions by L. interrogans correlated with calcium homeostasis deregulation and activation of calmodulin and MLCK kinases. Obtaining the in vivo L. interrogans transcriptomic profile allowed for the identification of two leptospiral toxin-like proteins of the Virulence-Modifying (VM) family. We established their essentiality in Leptospira pathogenicity and their involvement in regulating the calcium-induced calmodulin/MLCK signaling pathway, leading to cell–cell junction disassembly and promoting paracellular transmigration through host tissues. These findings revealed a unique mechanism by which an extracellular pathogen alters host calcium signaling to disrupt cell barriers for facilitating its dissemination and colonization inside the host.

Results
Dual RNA-Seq of L. interrogans-infected hamsters
To define the optimal condition for performing dual RNA-Seq in the model of human acute leptospirosis, Golden Syrian hamsters were infected with 108 L. interrogans by intraperitoneal route. This model was previously described to ensure reproducible susceptibility to lethal Leptospira infection and to reliably induce severe multi-organ pathology of human leptospirosis7–9. In these conditions, colonization of L. interrogans was detectable in liver and kidneys at 1-day post-infection (dpi), with signs of morbidity apparent at 4 dpi (Fig. 1A, B). To assess transcriptional changes at early and late stages of infection, we performed RNA sequencing of the host and Leptospira on liver and kidney tissues collected at 1 and 3 dpi (Fig. 1C). Initial sequencing, performed at standard read depths (~40 million reads/sample), revealed that more than 99.9% of total reads mapped to hamster genome, allowing for the identification of up to 984 differentially expressed genes (DEGs, p.adj<0.05; |Log2FC|>1) (Fig. 1D, Supplementary Fig. 1A). Among all conditions tested, the highest proportion of Leptospira reads was obtained by sequencing RNAs isolated from liver tissue at 3 dpi, detecting the expression of only 85 Leptospira genes (Supplementary Fig. 1A). To overcome this limitation and achieve higher Leptospira transcriptome coverage, RNAs isolated from liver at 3 dpi were deep-sequenced (~400 million reads/sample). This approach significantly increased the proportion of Leptospira genes detected from 4.57% of all expressed genes encoded in the bacterial genome to 79.34%, with a robust and well-distributed read count across Leptospira genome (Fig. 1E, Supplementary Fig. 1B–E). Thus, this experimental design provides a comprehensive and robust dual RNA sequencing profile encompassing both the host and pathogen transcriptomic responses.

Transcriptomic host response upon Leptospira infection
Principal component analysis (PCA) of the RNA-Seq dataset revealed distinct clustering of infected compared to uninfected samples at 3 dpi, while no separation was observed at 1 dpi, in liver as well as in kidney tissues (Fig. 1D). Despite the detection of a significant L. interrogans load at 1 dpi (Fig. 1B), the host transcriptional response was minimal, with only 4 and 8 DEGs (p.adj<0.05; |Log2FC|>1) in the liver and kidneys, respectively (Fig. 1D, Supplementary Fig. 2A, Supplementary Data 1). By 3 dpi, the transcriptional response was increased significantly, with 984 and 72 DEGs (p.adj<0.05; |Log2FC|>1) in the liver and kidneys, respectively (Fig. 1D, Supplementary Fig. 2A, Supplementary Data 1). These findings indicate that L. interrogans do not elicit an early host transcriptional response, even at a high infectious dose (108), given a LD50 of 10–100 leptospires10.

At 3 dpi, both liver and kidney exhibited transcriptional changes associated with inflammation, cell–cell junctions, and cytoskeletal organization (Fig. 2A, Supplementary Fig. 2B–D and Supplementary Data 2). Pathway enrichment analysis revealed upregulation of cytokine-cytokine receptor interaction (e.g., gdf15, ccl2, cxcl10, il1b) and chemokine signaling (fgr, hck, stat3), highlighting a common inflammatory response in both organs (Fig. 2B). Genes involved in the actin cytoskeleton, focal adhesions, tight junctions, and cell adhesion were also upregulated, including genes encoding integrins (itgal in both liver and kidney; itgb6, itgav, itga5, itga3 in liver; and itga4, itgb8 in kidney), as well as actin-related proteins (mlck, actg1 in liver; mapk3, pip5k1n in kidneys; Fig. 2C, Supplementary Fig. 2B–D and Supplementary Data 2). The differential expression of genes involved in inflammation (cxcl10, ccl2, fgr, nfκb1), calcium signaling (cd38, mcoln2, mylk, calm1), and actin/cell–cell junction (actb, itgal, arpc1b, jun, tjp2, cdh1) in liver and/or kidney were confirmed by RT-qPCR (Supplementary Fig. 2E, F).

In addition to the transcriptional responses shared by the liver and kidneys, organ-specific differences emerged. In the liver, transcriptional changes were more pronounced, with strong upregulation of inflammatory pathways, protein processing in the endoplasmic reticulum and cell–cell junction pathways (Fig. 2A, Supplementary Fig. 2 and Supplementary Data 2). In addition, we observed an upregulation of the calcium signaling pathway and genes encoding factors related to cell–cell junction integrity such as cadherin (cdh1) and tight junction protein (tjp2, encoding ZO-2). In contrast, the kidneys showed a more restricted transcriptional response, with fewer differentially expressed genes (Fig. 1D). A key distinction was the downregulation of specific metabolism-related pathways in the two organs. The liver response showed a specific downregulation of amino acid and fatty acid metabolism, whereas the kidney response was associated with a downregulation of oxidative phosphorylation (Fig. 2A, Supplementary Fig. 2 and Supplementary Data 2). Taken together, these findings indicate that, upon leptospiral infection, while both organs activate the inflammatory response and cell–cell junction pathways, the liver undergoes a more extensive transcriptional shift.

Leptospira transcriptional profile in vivo
Analysis of the transcriptomic response of L. interrogans in liver of infected hamsters showed that the expression of 1685 Leptospira genes (59% of total ORFs) was affected in vivo compared to in vitro condition, with 352 and 1333 up- and downregulated genes (p.adj<0.05; |Log2FC|>1), respectively (Fig. 3A, Supplementary Data 3). Pathway enrichment analysis indicated that upregulated genes were predominantly linked to bacterial secretion system, O-antigen biosynthesis and key metabolic pathways such as carbon, amino acid, and porphyrin metabolisms (Fig. 3B, Supplementary Fig. 3 and Supplementary Data 4). Notably, several genes encoding molecular chaperones involved in the proteotoxic stress response, such as groES, groEL, hsp15, hsp20, dnaK, dnaJ, and grpE were among the most highly upregulated (Fig. 3C). Additionally, the expression of several virulence-associated genes was significantly increased, including two genes encoding members of the Virulence-Modifying (VM) family, sph genes (encoding sphingomyelinase C), ligA and ligB (coding for immunoglobulin-like proteins A and B), colA (encoding collagenase A) and LIMLP_09380 (encoding a hypothetical membrane protein)11.

By contrast, genes involved in flagellar assembly, bacterial chemotaxis and two-component systems were downregulated (Fig. 3B, Supplementary Fig. 3). This included the decreased expression of a cluster (LIMLP_07420-07460) containing several genes associated with chemotaxis, such as mcpa and cheABDRWY and of several histidine kinase-encoding genes (LIMLP_11010, LIMLP_10185, LIMLP_10190, LIMLP_16820) (Fig. 3C). In addition, we observed the downregulation of genes associated with lipid, carbohydrate and coenzyme transport and metabolism (Supplementary Data 4).

Altogether, these findings revealed a major transcriptional reprogramming of L. interrogans in vivo, highlighting stress adaptation, metabolic shift and virulence induction as key features of adaptation to the host environment.

L. interrogans induces actin reorganization and disrupts epithelial cell junctions in the host
The host transcriptional response suggested cell–cell junction regulation during leptospiral infection. We evaluated whether changes in expression of genes involved in this process correlated with F-actin rearrangement and cell junction disruption in human epithelial cells infected by L. interrogans. Live-cell and immunofluorescence microscopy showed that human epithelial cells infected with L. interrogans exhibited dispersion and a less expanded morphology with actin staining at 24 hpi, resulting in reduced cell–cell contacts (Fig. 4A–C). Such phenomenon was not observed in cells infected with the saprophytic species L. biflexa (Supplementary Fig. 4). Importantly, no epithelial cell death was observed for at least 48h pi (Fig. 4D, Supplementary Fig. 5A), indicating that actin rearrangement was not related to cell death. Immunoblot analysis showed increased cellular contents of actin nucleation and polymerization factors (Profilin-1, WAVE-2, and phosphorylated-Rac1), as well as of the actin reorganization factor phosphorylated Cofilin (Supplementary Fig. 5B). Of note, genes encoding these factors were upregulated upon Leptospira infection in liver (Supplementary Table 1).

In addition, infected cells exhibited at 24 hpi a decrease of cadherin protein in plasma membrane (1.6-fold reduction) (Fig. 5A) and a reduction in tight junction proteins ZO-1, ZO-2, and JAM-1 compared to uninfected controls (1.5-, 1.4-, and 1.6-fold reductions for ZO-1, ZO-2, and JAM-1, respectively, as measured by immunofluorescence, Fig. 5B, C). Decrease in ZO-1 and ZO-2 was also observed by immunoblot (Fig. 5D). Experiments using the human hepatic cell line HepG2 confirmed these observations, showing no cell death upon L. interrogans infection together with decreased cadherin at the plasma membrane and reduced ZO-1 levels (Supplementary Fig. 6). Interestingly, infection by L. interrogans did not lead to a decrease of the gap junction protein Connexin-43 cellular content but to a change in its localization. Indeed, while Connexin-43 was primarily localized at the plasma membrane in uninfected cells, it accumulated in the endoplasmic reticulum (ER) in infected cells (Fig. 5E–G). Finally, disruption of cell junctions upon infection by L. interrogans correlated with a greater ability to cross epithelial monolayers, compared with the saprophytic L. biflexa which lacks the capacity to disrupt cell–cell junction integrity (Fig. 5H and Supplementary Fig. 4). Altogether, these results demonstrate that infection by L. interrogans leads to a profound reorganization of the cytoskeleton and compromises epithelial barrier integrity probably by triggering downregulation or mislocalization of tight, adherens and gap junction proteins.

ER stress response and its implications in Leptospira-infected cells
The host transcriptional response and RT-qPCR analysis under in vitro condition revealed an upregulation of genes associated with the ER stress pathway during infection by L. interrogans (Fig. 2A and Supplementary Fig. 7A, B). Given the correlation between ER stress and the disruption of cell–cell junctions12–14, we evaluated the modulation of ER activity upon infection by L. interrogans. Using a fluorescent ER marker specific to the ATP-sensitive K+ channels of this organelle, we observed reduced signal intensity at 6, 24 and 48 h pi in infected cells (Supplementary Fig. 7C, D) with increased phosphorylation of PERK and IRE-1 (Supplementary Fig. 7E), suggesting altered ER function. To determine whether this ER stress response contributed to cell junction disruption, infected cells were treated with different ER stress inhibitors. However, none of the inhibitors restored cell–cell junction integrity in L. interrogans-infected cells (Supplementary Fig. 7F, G). Therefore, while L. interrogans induces an ER stress response probably mediated by PERK and IRE-1 pathways, this response does not substantially contribute to cell–cell junction disruption.

Calcium signaling participates in cell–cell junction disruption in L. interrogans-infected cells
Given the upregulation of calcium-related genes upon L. interrogans infection (Fig. 2A, C) and the established role of Ca2+ signaling in cytoskeletal dynamics15, we investigated whether this cation accumulation was functionally linked to junction disruption, we treated infected cells with BAPTA-AM, a cell-permeable Ca2+ chelator. This treatment effectively limited the infection-induced Ca2+ increase, reduced cell junction dispersion and increased ZO-1 and cadherin localization at cell–cell junctions compared to untreated infected cells (Fig. 7A–C, Supplementary Figs. 8 and 9A). These results suggest that intracellular Ca2+ plays an important role in Leptospira-induced junction disruption.

TJ and AJ integrity depends on the interaction between the TJ or AJ protein complexes and the perijunctional actomyosin ring16, a process controlled by calmodulin-dependent myosin light chain kinase (MLCK) activity. MLCK activation leads to myosin phosphorylation, resulting in increased cell junctions permeability in epithelial cells17,18. L. interrogans-infected epithelial cells exhibited elevated level of phosphorylated myosin compared to uninfected controls (Fig. 7D). Inhibition of calmodulin or MLCK using KN-93 or ML-7 inhibitors, respectively, partially restored ZO-1 and cadherin localization at cell–cell junctions and reduced cell dispersion in infected cells (Fig. 7E–G, Supplementary Fig. 9B). Notably, this inhibition also impaired L. interrogans transmigration across the epithelial monolayer (Fig. 7H). These findings indicate that L. interrogans exploits host Ca2+ signaling to activate the calmodulin-MLCK pathway, leading to junctional disassembly and enhanced paracellular transmigration.

Two Leptospira virulence-modifying (VM) proteins are required for virulence and disruption of cell–cell junction through calcium signaling pathway
The most highly upregulated leptospiral genes in vivo encode two Virulence-Modifying (VM) proteins (LIMLP_11655, Log2FC=7.9; LIMLP_11660, Log2FC=8.9; Fig. 3A, Supplementary Data 3). These VM proteins belong to the DUF1561 family, whose function remains unknown. They possess toxin-like features, including a ricin B-like (or Cards B-like) domain potentially mediating interaction with host receptors, and a C-terminal domain of unknown function19 (Supplementary Fig. 10A). In L. interrogans, LIMLP_11655 and LIMLP_11660 genes are adjacent (Supplementary Fig. 10B).

Orthologs of VMs are present in other pathogenic bacteria such as Bartonella, Helicobacter and Campylobacter (Supplementary Fig. 10C–E). L. interrogans harbors a total of thirteen VM proteins, all exclusive to pathogenic Leptospira species11. Comparative sequence identity analyses indicated that LIMLP_11655 and LIMLP_11660 exhibit greater sequence similarity to each other than to other VM proteins (Supplementary Fig. 10F). Notably, LIMLP_11655 and LIMLP_11660 were the only two VM genes significantly upregulated in vivo (Supplementary Fig. 10F).

We had already demonstrated that silencing LIMLP_11655 (hereafter referred to as dcas9-LIMLP_11655 mutant) led to an attenuated L. interrogans virulence in hamsters11. Likewise, LIMLP_11660 inactivation led to complete loss of virulence, which was restored upon complementation (Fig. 8). Furthermore, when both VM genes were inactivated or silenced (hereafter referred to as LIMLP_11660+dcas9-LIMLP_11655 mutant), L. interrogans virulence was lost. Importantly, all these strains exhibited growth rates comparable to that of the WT when cultivated in EMJH medium (Supplementary Fig. 11), ensuring that the observed decreased virulence could not be attributed to growth defects. Additionally, all three mutants (LIMLP_11660, dcas9-LIMLP_11655 and LIMLP_11660+dcas9-LIMLP_11655) exhibited a reduced ability to transmigrate across epithelial cells at 3h pi (Fig. 9A).

To investigate whether LIMLP_11660 and LIMLP_11655 contribute to epithelial cell barrier disruption as secreted factors, we tested whether supernatants from WT and mutant strains (LIMLP_11660, dcas9-LIMLP_11655, and LIMLP_11660+dcas9-LIMLP_11655) could promote the transmigration of saprophytic L. biflexa, normally unable of crossing the epithelial barrier (as seen in Fig. 5H). Incubation of epithelial cells with L. interrogans WT supernatant enhanced L. biflexa translocation, indicating that L. interrogans secretes factors capable of disrupting epithelial junctions (Fig. 9B). Conversely, supernatants from VM mutants (LIMLP_11660, dcas9-LIMLP_11655 and LIMLP_11660+dcas9-LIMLP_11655) exhibited a lower ability to promote L. biflexa transmigration and to reduce cadherin and ZO-1 levels at epithelial junctions (Fig. 9B, C, Supplementary Fig. 12A). Complementation of LIMLP_11660 mutant restored both the ability to promote L. biflexa transmigration as well as reduced cadherin and ZO-1 amounts at the cell–cell junction to a comparable manner as the WT (Fig. 9B, C, Supplementary Fig. 12A). However, infection with VM mutants still led to the disruption of cadherin and ZO-1 in epithelial cells (Supplementary Fig. 12B, C), except for the LIMLP_11660+dcas9-LIMLP_11655 mutant where we observed a slight increase of cadherin and ZO-1 localization in cell junctions. Similar alterations were also observed in hepatocyte cells using incubation with supernatants or bacterial infection (Supplementary Fig. 13).

To confirm VM secretion, we complemented the LIMLP_11660 mutant with a Flag-tagged allele (LIMLP_11660comp-C1) and we successfully detected the Flag-tagged LIMLP_11660 protein in the L. interrogans supernatant (Fig. 9D). Furthermore, when epithelial cells were exposed to the supernatant from LIMLP_11660comp-C1 strain for 1 and 6h, the Flag-tagged protein was detected in a total lysate of the epithelial cells, and this correlated with a reduced ZO-1 amount at the cell–cell junction comparable to that observed upon incubation with the WT strain supernatant (Fig. 9E, Supplementary Fig. 14). These findings demonstrate that LIMLP_11660-encoded VM is secreted by L. interrogans, associates with and modulates the host cell environment.

These results suggest that these two VM proteins exert their role in Leptospira pathogenicity by being secreted, binding to the host cell plasma membrane, possibly by being internalized within host cells, disrupting TJ and AJ, and promoting Leptospira transmigrating through epithelial cells layers.

We next examined whether LIMLP_11660 and LIMLP_11655 promoted TJ and AJ disruption by altering Ca2+-calmodulin-MLCK signaling. Incubation with supernatants of LIMLP_11660 or dcas9-LIMLP_11655 mutant cultures significantly impaired the rise of intracellular Ca2+ concentration by 4 and 8.5 times, respectively, compared to WT. LIMLP_11660 complementation restored the increase in intracellular Ca2+ concentration observed upon incubation with the supernatant of WT (Fig. 9F, Supplementary Fig. 15A). Of note, these effects were less pronounced when infecting epithelial cells with corresponding whole bacterial strains (Supplementary Fig. 15B). Moreover, incubating cells with supernatants from VM mutant cultures correlated with reduced phosphorylation of myosin, a mediator of TJ and AJ integrity (Supplementary Fig. 16). Collectively, these findings demonstrate that LIMLP_11660 and LIMLP_11655 act upstream of the Ca2+-calmodulin-MLCK pathway, facilitating TJ and AJ disruption through calcium signaling activation.

Methods
Ethics statement
Protocols for animal experiments are conformed to the guidelines of the Animal Care and Use Committees of the Institut Pasteur (Comité d’éthique d’expérimentation animale CETEA#220016), agreed by the French Ministry of Agriculture. All animal procedures carried out in our study were performed in accordance with the European Union legislation for the protection of animals used for scientific purposes (Directive 2010/63/EU).

Bacterial strains and culture conditions
Strains and plasmids used in this study are listed in Supplementary Tables 3 and 4. WT L. interrogans serovar Manilae strain L495 and mutants, and WT L. biflexa serovar Patoc strain Patoc1 (Paris strain), were cultivated aerobically in Ellighausen-McCullough-Johnson-Harris liquid medium (EMJH) at 30°C with shaking at 100 rpm. π1 and β2163 E. coli strains were cultivated in Luria-Bertani medium with shaking at 37°C in the presence of 0.3mM thymidine or diaminopimelic acid (Sigma-Aldrich), respectively. Spectinomycin was added to the media at 50µg/ml when needed.

In vivo animal infection
Four-week-old male Golden Syrian hamsters (RjHan:AURA, Janvier Labs) (n=4) were infected via intraperitoneal injection with 108 L. interrogans WT and mutant strains, with the number of bacteria enumerated using a Petroff-Hausser counting chamber. The animals were monitored daily and euthanized by carbon dioxide inhalation when they reached the predefined endpoint criteria (sign of distress). To assess leptospiral load, samples of kidney and liver tissue were harvested, and DNA was extracted using the Tissue DNA Purification kit (Maxwell, Promega). The bacterial burden and host DNA concentration were determined by quantitative polymerase chain reaction (qPCR) using the SsoFast EvaGreen Supermix assay (Bio-Rad) with primers targeting the flaB2 (LIMLP_09410) and gapdh genes, respectively (Supplementary Table 5). The leptospiral load was expressed as genomic equivalents (GEq) per microgram of host DNA.

RNA extraction, library preparation and sequencing
Liver and kidneys tissues of hamsters (infected or not by L. interrogans as described above) were resuspended in the QIAzol lysis reagent (Qiagen) with 5mm stainless-steel beads (Qiagen) and homogenized with the Qiagen Tissue Lyser (two cycles of one-minute at 25 Hz, with a one-minute break between cycles in ice). Total RNAs were extracted from homogenized samples using the RNeasy Mini Kit (Qiagen), with a DNAse digestion (Qiagen) incorporated in the protocol. RNAs were also isolated from exponentially growing L. interrogans cultivated in EMJH medium (1010 leptospires). Bacterial pellets were resuspended in the QIAzol lysis reagent and total RNAs were extracted using a similar procedure (RNeasy Mini Kit with a DNAse treatment). The quality of all RNA samples was evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies) to ascertain a RNA integrity number (RIN) higher than 8.5.

Sequencing libraries were constructed using an Illumina Stranded Total RNA Prep Ligation using the RibZero Plus kit (Illumina, USA) with custom primer design on rRNA following the supplier’s recommendations. The efficiency of rRNA depletion for both Leptospira and hamster samples was evaluated by shallow sequencing, revealing residual rRNA levels of approximately 5% in Leptospira and 1–5% in hamster samples, confirming efficient depletion in both species. RNA sequencing was performed with the Illumina NextSeq 2000 and NovaSeq X+ platform for a target of 10M reads/sample for bacteria, 30M reads/sample for hamster and 400M reads/sample for deep sequencing (performed on RNA samples isolated from 3 hamsters infected by L. interrogans). The RNA-seq analysis was performed with Sequana 0.15.343 using the RNA-seq pipeline 0.16 (https://github.com/sequana/sequana_rnaseq) built on top of Snakemake 7.32.444. Briefly, reads were trimmed from adapters using Fastp 0.22.045 then mapped to the L. interrogans serovar Manilae genome (NZ_CP011931.1) and Golden Syrian hamsters genome (KB708127.1) using Bowtie246 and STAR47. FeatureCounts 2.0.148 was used to produce the count matrix, assigning reads to features using the corresponding annotation from Ensembl with strand-specificity information. To verify the absence of cross-mapping between host and pathogen reads, uninfected hamster RNA-seq libraries were aligned to the L. interrogans genome, which only yielded a few hits restricted to rRNA loci and no assignments to protein-coding genes, confirming the negligible cross-mapping. Quality control statistics were summarized using MultiQC 1.1149. Statistical analysis on the count matrix was performed to identify differentially regulated genes. Clustering of transcriptomic profiles were assessed using a Principal Component Analysis (PCA). Differential expression testing was conducted using DESeq2 library 1.34.050 scripts indicating the significance (Benjamini-Hochberg adjusted p-values, false discovery rate FDR < 0.05) and the effect size (fold-change) for each comparison.

Gene ontology (GO) enrichment analyses were performed using the Cytoscape app ClueGO (version 2.5.3)51, with the following parameters: only pathways with pV≤0.01, Minimum GO level=3, Maximum GO level=8, Min GO family >1, minimum number of genes associated to GO term=5, and minimum percentage of genes associated to GO term=8. Enrichment p-values were calculated using a hypergeometric test (p-value < 0.05, Bonferroni corrected).

Gene expression by RT-qPCR
Reverse transcription of mRNA to cDNA was performed using the iScript cDNA Synthesis kit (Bio-Rad), followed by cDNA amplification using the SsoFast EvaGreen Supermix (Bio-Rad). All primers used in this study are listed in Supplementary Table 5. Reactions were performed using the CFX96 real-time PCR detection system (Bio-Rad). The relative gene expression levels were assessed according to the 2−ΔΔCt method using flab2 or gapdh as reference gene for L. interrogans or human cells, respectively.

Cell culture and infection
HEK-293T and HepG2 cells were cultured in Minimum Essential Medium Eagle (MEME) (Sigma) supplemented with 10% heat-inactivated fetal bovine serum (Sigma) and 2mM L-glutamine (Gibco). Leptospira strains were diluted in this complete cell culture media prior the infection, at a multiplicity of infection (MOI) of 100:1. All chemical compounds used on cells were added 1h before the experiment and maintained throughout the duration specified in the figure legend. BAPTA-AM, KN-93, and ML-7 inhibitors were used at 10, 2, and 7µM, respectively.

Indirect immunofluorescence and live-cell imaging of epithelial cells and hepatocytes
Epithelial cells and hepatocytes were cultured on glass coverslips (SPL) coated with L-lysine (Sigma) at a concentration of 0.01% in water for a period of 40 min at 37°C. The coverslips were rinsed twice with PBS to remove excess of L-lysine, and the cells were seeded. At the indicated times, the cells were fixed with 4% paraformaldehyde for 15min at room temperature (RT) and subsequently incubated for 10 min in 0.5% saponin (Sigma) in PBS and for 1h in 1% BSA (Sigma) and 0.075% saponin in PBS. The cells were incubated overnight at 4°C with the anti-ZO1 (13663; Cell Signaling), anti-ZO2 (2847; Cell Signaling), anti-JAM1 (82196; Cell Signaling), anti-pan-cadherin (PA5-16766; ThermoFisher) or anti-connexin-43 (3512; Cell Signaling) antibodies at 1:100. Cells were washed and incubated for 1h with Alexa Fluor 555 or 488 antibody (ThermoFisher) at 1:500. Filamentous actin was labeled with ActiStain 555 phalloidin (PHDH1; Tebu) at 100nM during 30min at room temperature. The nuclei were then stained with DAPI (1µg/mL; ThermoFisher) for 10min and mounted on a glass side using Fluoromount mounting medium (ThermoFisher). Fluorescence was analyzed using a Leica TCS SP8 Confocal System. The quantification was performed using Icy software in a non-blinded manner.

For live-cell imaging, cells were seeded on an µ-Slide 8 Well (Ibidi). To assess ER activity, cells were labeled with ER-Tracker Red (ThermoFisher) at a concentration of 1µM for 30min at 37°C, followed by two washes with FluoroBrite DMEM medium (ThermoFisher) supplemented with 10% heat-inactivated fetal bovine serum (Sigma) and 2mM L-glutamine (Gibco). For the measurement of intracellular calcium levels, cells were incubated with Fluo-8 AM (Abcam) at a concentration of 4µM for 1h, after which they were washed twice with complete FluoroBrite DMEM medium prior to the analysis time. Brightfield and fluorescence microscopy were conducted using an inverted widefield microscope equipped with an LED colibris and a mercury lamp. Images were acquired using a dual camera sCMOS Hamamatsu ORCA FLASH. Cell dispersion and fluorescence quantification were performed using Icy software (Version 3.0). For each experimental condition, at least 3 random fields and 100 cells were analyzed. Cell boundaries were defined using filamentous actin staining and individual ROIs (region-of-interest) were generated for each cell, where the mean fluorescence intensity of ZO-1, ZO-2, JAM-1, pan-cadherin and connexin-43 was obtained. For pan-cadherin and connexin-43, fluorescence intensity was separately quantified at the plasma membrane and within the ER compartment, defined by FM 4-64FX (5µg/mL; ThermoFisher) and ER-Tracker staining, respectively.

Cell-free area was quantified from confocal images by analyzing 10 randomly selected fields per condition. First, the total cell area within each field was segmented, regions not occupied by cells were identified as cell-free areas and the averaged distance between medial axis of cell-free area and the adjacent cell-containing areas was calculated as an index of spacing between cells. To account for differences in cell density, the cell-free area was normalized to the number of cells in each field, providing a relative measure of intercellular spacing per cell.

Cell viability assays
Cell viability was determined using the MTT assay kit (Abcam), according to manufacturer’s instructions. Cell death was evaluated with the Dead Cell Apoptosis Kit with Annexin V Alexa Fluor 488 and Propidium iodide (V13241; ThermoFisher) according to the manufacturer’s instructions. Fluorescence was analyzed using a CytoFLEX Flow Cytometer (Beckman Coulter). At least 10,000 events per sample were recorded. A positive control corresponding to cells incubated at 55°C for 20 min prior staining is included for the analysis. The analysis was performed using the Floreada.io website (version SIMD).

Immunoblot
Immunoblot was performed as described above. Total extracts of epithelial cells or Leptospira strains were obtained by sonication in a lysis buffer containing 25mM Tris pH 7.5, 100mM KCl, 2mM EDTA, 5mM DTT and a protease inhibitor cocktails (cOmplete Mini EDTA-free, Roche). 10µg of total proteins were loaded on a 12% SDS-PAGE (Mini-PROTEAN TGX Stain-Free precast gels, Biorad) and transferred onto nitrocellulose membrane. The membranes were blocked with PBS Tween 20 0.1% containing 5% BSA for 1h at RT and incubated overnight at 4°C with the antibodies anti-ZO1 (13663; Cell Signaling), anti-ZO2 (2847; Cell Signaling), anti-Profilin-1 (3246; Cell Signaling), anti-WAVE-2 (3659; Cell Signaling), anti-pRac1 (2461; Cell Signaling), anti-pCofilin (3313; Cell Signaling), anti-Cofilin (5175; Cell Signaling), anti-GAPDH (5174; Cell Signaling), anti-MLC2 (8505; Cell Signaling), anti-pMLC2 (3671; Cell Signaling), anti-PERK (3192; Cell Signaling), anti-pPERK (PA5-40294; ThermoFisher), anti-IRE1 (3294; Cell Signaling), anti-pIRE1 (PA1-16927; ThermoFisher) and anti-3xFLAG (87537; Cell Signaling) at a 1:1000 dilution. Membranes were washed in PBS-0.1% Tween 20 and incubated at RT with secondary HRP-conjugated antibody (7074S; Cell Signaling) at a 1:1000 dilution for 1h. Detection was performed with SuperSignal West Femto Maximum Sensitivity Substrate (ThermoFisher). The molecular mass markers (in kilodaltons) are indicated on the side of each immunoblot panel. Quantification of band intensities was performed using ImageJ software.

Translocation through epithelial cells
5×104 HEK293T cells in 300µL of complete MEME were introduced into 12-mm-diameter Transwell filter units with 3µm pores (COSTAR). The monolayers were incubated at 37°C in 5% CO2 for a period of three days until the transepithelial resistance reached a range of 200 and 300 Ω/cm2 using an epithelial voltammeter. The monolayers were infected with leptospires at a MOI of 100. At the indicated times, the capacity of Leptospira strains to translocate across the epithelial cell barrier was evaluated by extracting the DNA from the bacterial suspension recovered in the lower chamber (300µL) using the Maxwell RSC Cultured Cells DNA Kit and the Maxwell instrument (Promega). The concentration of leptospires was determined by qPCR with the SsoFast EvaGreen Supermix assay (Bio-Rad) using flaB2 (LIMLP_09410) for L. interrogans and flaB4 (LEPBIa1589) for L. biflexa (Supplementary Table 5). The capacity of leptospires to translocate HEK T293T polarized monolayers (% of Leptospira recovery) was quantified by calculating the proportion of leptospires present in the lower chamber relative to the initial inoculum at each time points.

Complementation of L. interrogans mutants
Complementation of the LIMLP_11660 mutant was performed by expressing LIMLP_11660 using the replicative vector pMaOri52 (Supplementary Table 4) under the control of its native promoter. For this, LIMLP_11660 ORF and the 300bp upstream region were amplified from genomic DNA of L. interrogans (using Comp LIMLP_11660 primer set; Supplementary Table 5) and cloned between the SacI and NotI restriction sites in the pMaOri vector. The absence of mutations in the resulting plasmid (pMaOri-LIMLP_11660, Supplementary Table 4) was confirmed by DNA sequencing (Eurofins). Then, the plasmid pMaOri-LIMLP_11660 was introduced into LIMLP_11660 mutant strain by conjugation using the E. coli β2163 conjugating strain, as previously described53. Leptospira conjugants were selected on EMJH agar plates containing 50µg/mL spectinomycin. WT and LIMLP_11660 mutant strains containing the empty pMaOri vector were used as controls. Expression of the Flag-tagged LIMLP_11660 variants was performed by introducing a 3xFlag tag in frame at the 3’ of the ORF (LIMLP_11660comp-C1) or by replacing the peptide signal (LIMLP_11660comp-C2) using restriction-free cloning method in the plasmid pMaOri-LIMLP_11660 with the primers LIMLP_11660-C1 and LIMLP_11660-C2 (Supplementary Table 5). Conjugation in L. interrogans was performed as described above. Gene silencing of LIMLP_11655 into the LIMLP_11660 mutant was performed by introducing the leptospiral replicative vector pMaori.dCas9_sgLIMLP_11655 by conjugation as previously described11.

Supernatant isolation of L. interrogans
Supernatants were obtained from exponentially growing L. interrogans cultivated in MEME medium (Sigma) supplemented with 10% heat-inactivated fetal bovine serum (Sigma) and 2mM L-glutamine (Gibco) overnight at 37°C. Bacteria were centrifuged at 5000×g for 15min and the supernatants were filtered through a 300 kDa membrane (Sartorius). Unless otherwise indicated in the Figure legends, cells were incubated with supernatants for 24h (in infection experiments or in Transwell assays).

In silico analysis of LIMLP_11660 and LIMLP_11655
The sequence similarity of the LIMLP_11660 protein from L. interrogans serovar Manilae was determined using UniProtKB reference proteomes and Swiss-Prot databases. All significant hits, defined as the best match for each species with an e-value < 0.00033 and a protein identity > 20%, were retained. Sequence alignment of the proteins and percent identity matrix were performed using Clustal-Omega (1.2.4). Tree inference was achieved with IQ-TREE v2.0.6 under the best-fitted model.

The domain models were created using the LIMLP_11660 or LIMLP_11655 protein sequences via the SWISS-MODEL Workspace server (https://swissmodel.expasy.org/, SMTL version 2023-04-05, PDB release 2023-03-31).

Statistics and reproducibility
Statistical analyses were conducted using GraphPad Prism (version 9.5.1). Unless otherwise stated, all experiments were performed in three independent biological replicates. All microscopy images shown are representative of three independent biological replicates. In all graphs, data are presented as mean±SD. Centre line, median, box limits, the interquartile range ± min and max values were indicated in box and whisker plots. Information about specific statistical tests in each analysis can be found in the figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study Giraud-Gatineau et al. provide the host-and-pathogen transcriptional responses of the most common pathogenic\nLeptospira species in vivo in an animal model of severe leptospirosis. They used dual RNA-sequencing from tissues of\ninfected hamsters to compare transcriptomes of infected vs uninfected tissues and did deep-RNA-sequencing from tissues to\nenrich bacterial reads that enable comparison of Leptospira transcriptional profiles in vivo and in vitro. The deep-dual-\nRNAseq method is an example on how to overcome RNA-seq technical difficulties with strict extracellular pathogens where\nselection of infected cells cannot be used. They followed up with a suite of cell-bio, molecular biology and live-cell imaging\ntechniques to describe how two L. interrogans virulence-modifying proteins promote calcium homeostasis and disruption of\ncell-cell junctions. The study approach is well-designed, comprehensive, technically rigorous and highly impactful for the\nLeptospirosis field as well as other pathogens that target paracellular permeability (e.g. H. pylori, Bartonella). The\nmechanistic part of the study is nicely executed. Minor weaknesses include not using blood as a sample in addition to liver\nand kidney, and not including data on validation of the transcriptional identified host proteins. If experiments are not feasible\nat this juncture these points can be included in study limitations.\nComments for the authors:\nA) On sequencing, Sequana pipeline, built on Snakemake, provides reproducibility and traceability; detailed tools listed are\nstandard and accepted in the field.\n1) The authors mention using “custom primer design on rRNA” with the RiboZero Plus kit. As this is a key step in ensuring\nadequate depletion of Leptospira rRNA, a clarification if these were custom-designed capture probes specific to Leptospira\nrRNA (e.g., 16S, 23S), and whether hamster rRNA was fully targeted by the standard probe set could be beneficial.\nAdditionally, was the efficiency of rRNA depletion for both species evaluated (e.g., Bioanalyzer, % rRNA reads)?\n2)The authors report detecting 4.57% to 79.34% of Leptospira genes, but the criteria for “gene detection” are not specified.\nRecommend a clarifying state on threshold used (e.g., ≥1 read, CPM > 1) to improve transparency and enable reproducibility\nof the transcriptome coverage claims. It would also be of interest to include the percentage of total reads mapping to the\nbacterial genome before and after deep sequencing.\n3)Dual RNA-seq introduces the possibility of cross-mapping between host and pathogen reads, particularly in low-\nabundance pathogen RNA contexts. While the authors mapped reads to both genomes using STAR and Bowtie2, the\nmanuscript would benefit from clarification on whether steps were taken to prevent or quantify cross-mapping artifacts — for\nexample, through combined genome indexing or ambiguity filtering. This would further strengthen confidence in the\nassignment of reads to Leptospira or hamster origin.\nB) On Other Methods:\n1. Animal Model: The use of 4-week-old hamsters and a high infectious dose for this species should be explained in the\ncontext of disease modeling and its translational relevance.\n2. Serovar: Since L. interrogans serovar Manilae was used it would be interesting to know if the authors expect differences in\nRNAseq if another L. interrogans serovar was used, what about another species? Given the use of Manilae for infection, why\nwas lung not processed for RNA-seq?\n3. Transcriptomic Focus: incorporating blood transcriptomics could have added valuable insights\n4. In Vitro Infection Conditions: The manuscript lacks details regarding the infection protocol for L. interrogans in vitro.\nSpecifically, was the infection performed in complete media with serum? This could affect bacterial uptake and cellular\nresponses.\n5. In vitro analysis using HEC293T cells which is kidney cell line: it would be interesting to see results in a hepatocyte cell\nline (e.g.HEPG2) since the highest differences in vivo are seen in liver. Alternatively, this could be added to study limitations\n6. Calcium Signaling Assessment: The use of Fluo-8 dyes and microscopy to assess calcium signaling is limited in\nquantitative power. Flow cytometry could have provided a more accurate and robust quantification. Also, control image\nquality is poor, making interpretation difficult.\n7. F-actin Visualization: The antibody-based F-actin staining displays high background and non-specific signals,\ncomplicating interpretation of actin dynamics. Phalloidin staining, a widely used method in the field, offers more reliable\nvisualization of filamentous actin. From the confocal images, no clear changes in actin polymerization are evident. A\nreduced number of cells in infected groups complicates interpretation based on cell-free area quantification. Regarding\ncofilin analysis, please clarify whether the total and phosphorylated cofilin bands were obtained from the same blot.\n8. RAC1 Signaling Interpretation: The study reports changes at the phosphorylated RAC1 level, typically associated with\nenhanced actin polymerization. However, the manuscript claims actin disorganization, which appears contradictory. Please\nclarify, possibly by analyzing downstream effectors of RAC1 that influence actin dynamics.\n9. Adhesion Molecule Analysis: While discussing calcium signaling and epithelial barrier function, it is important to also\nexamine adhesion molecules such as ICAM, VCAM, cadherins, and catenins. The authors have cited relevant literature\nsupporting their role in Leptospiral pathogenesis, but no data is presented. This could be tested and/or added as a study\nlimitation.\n10. Cell Death Assessment: The authors report no evidence of epithelial cell death. However, no direct assays for cell death\npathways (e.g., apoptosis, necrosis) were performed. The MTT assay, being a colorimetric method for cell viability, cannot\ndefinitively rule out cell death. The authors should consider performing more specific assays (e.g., Annexin V/PI staining,\ncaspase activity, TUNEL assay) to substantiate cell death.", "reviewer_2": "GIRAUD-GATINEAU et al.\nIn this work, the authors, using dual RNA-Seq, determined the in vivo host-pathogen transcriptomic landscape upon\ninfection by the extracellular pathogen Leptospira interrogans. They found a novel mechanism of cell-cell junction disruption,\nwhere an increase in intracellular calcium triggered tight junction destabilization, by activating the calmodulin and myosin\nlight chain kinase signalization. Additionally, two novel bacterial effectors of the Virulence-Modifying (VM) proteins family,\nstructurally related to toxin-like proteins, were found. These proteins promoted modulation of calcium homeostasis and\ndisruption of cell-cell junctions, thereby allowing Leptospira translocation across epithelium barriers, tissue colonization and\npathogenicity. This is a very interesting and innovative study. The high-quality experimental data support the authors'\nconclusions.", "reviewer_3": "The authors report host and bacterial transcriptomes during in vivo infection of L. interrogans using a dual RNAseq\napproach. They find an increase in calcium signalling and associated changes in tight junction destabilization and identified\ntwo VM protein family effectors that that are linked to cell junction disruption.\nOverall, the paper is well written and data well presented. The finding that the VM effectors are associated with disruption of\ncell junctions is quite interesting. However, in general the data supporting some of the findings, for example, the\nmechanisms underlying calcium signalling, are not that convincing. Calcium signalling induced by other pathogens like\nPseudomonas leads to disruption of epithelial junctions, so this mechanism is not unique to Leptospira.\nMy specific concerns with the study are\n• Introduction: The authors claim in line 34 ‘the mechanisms employed by extracellular pathogens remain comparatively\nunderexplored’ and in line 44 that molecular mechanisms of cell-cell junction disruption are not understood. This is not true\nas there are numerous examples of virulence mechanisms at host-pathogen interfaces of extracellular pathogens like\nstaphylococcus, pseudomonas, clostridium etc. There are several bacterial toxins including staphylococcal, clostridial toxins\nthat have been shown to disrupt epithelial barriers.\n• Would be good to know the numbers of sequencing reads obtained from bacterial and the hamster host\n• Figure 4 essentially demonstrates that actin reorganisation and epithelial cell junction disruption is seen in human\nHEK293T cells. This section essentially is reporting what has already been previously reported in the literature.\n• Figure 5- Could the authors quantify fluorescence over time with the Fluo-8 AM staining assay? This would provide a non-\nmicroscopy-based quantification of intracellular calcium.\n• Please include quantification of band intensities from blots showing the phosphorylation of MCL2 blots/ normalised to\nMCL2 and the loading control. Data should typically be from at least three blots.\n• Most of the conclusions made on inhibitor effects depend on the ZO-1 staining and cell-free area quantification, which are\nboth microscopy-based. Were these microscopy quantifications performed blinded? Immunoblotting ZO-1 or quantifying\nanother marker would help strengthen these data.\n• The differences in the %leptospires recovered after translocation is lower, however the difference is quite nominal, and it is\nnot clear if the differences are statistically significant.\n• Figure 6. It is nice to see that the LIMLP_11660 mutant supernatants are affecting translocation, and ZO-1 staining.\nDo whole WT and mutant bacteria incubated with the cells also have similar effects? And also, is there an increased calcium\nsignalling seen with whole bacteria.\n• Line 256, states ‘infection’ with mutants, although the figure legend for 6G says supernatants were added to cells- please\nclarify.\n• I think it is possible that the proteins are bound to receptors on the cell surface- I don't think the data shown support the\nconclusion that the FLAG tagged protein is internalised. Have the authors tried to do immunofluorescence with anti-FLAG to\nstudy protein localisation in the host cell?\n• Methodology: No details were provided regarding the quantification of images- how many cells, fields were counted?\nIcy software was mentioned, no version and no details on what was quantified- for e.g., cell free area, how was this defined etc.\n• Statistics- unpaired t tests have been used in many figures, e.g. figure 1 B where there are multiple data sets being\ncompared-I believe an ANOVA or similar should be used here.", "reviewer_4": "This is a beautiful paper that uses dual RNAseq to identify bacterial and host pathways altered during in vivo infection (VM\nproteins and calcium signalling/actin cytoskeleton) and culminates with demonstrating a mechanistic link between the two.\nWhilst it does not yet show exactly what the molecular basis is – what is the cellular target of the two VM proteins and how\ndo they affect calcium signalling/cytoskeleton? – it is my opinion that these first findings presented here are substantial\nenough in scope to warrant publication in Nature Communications.\nMajor(ish) concerns:\n1. I am not convinced by the transwell assays used in Fig. 4L, 4J and 6B. As performed, it cannot definitely say that the\nincrease in bacteria in the lower chamber is due to migrated bacteria, rather than an increase in bacterial growth through\nreplication occurring over this time period. A better assay would be to infect the cell layers with the bacteria as done here,\nbut then test for passage of an unrelated, inert particle such as FITC-dextran, a protein, or an inactivated bacterium. This will\nseparate out permeability from cell growth. The version used in 6C is OK because this measures transfer of L. biflexa in\nmonolayers infected with L. interrrogans.\n2. There is not enough information about the in vitro sample to asses whether this is an appropriate control, and this is\nimportant as it forms the basis of all the comparative dualRNAseq analysis on bacterial transcripts. Was this isolated at\nexponential growth or stationary growth? Also, is it known (from the literature) how many of these genes differ at different\nstages of in vitro growth? i.e. which of these genes are generally variable to growth conditions, compared with those\nspecifically altered during in vivo infection?\nMinor comments:\n1. Line 86, at a first read it sounds like these percentages refer to the percentage of total in vivo reads that are bacterial. After\nreading more carefully it became apparent that this refers to the percentage of total genes encoded in the bacterial genome\nthat are expressed. It would be good to rephrase this to make it clearer to understand. Also it should be 4.57 not 4,57.\n2. Fig 2 – confusing to use red/blue for different organs in Fig 2C, as everywhere else it refers to up/down regulated.\n3. Fig 3C, it is not clear why these particular “poorly characterised” genes were selected for display here. Highest fold\nchange? Most interesting?\n4. Line 153 and Fig 4A, talk about actin rearrangement but this cannot be seen (at least to my eye) in the panel it refers to.\n5. Fig 4H – is there a reason why JAM-1 not included here?\n6. Lines 176-191 – this substantial section does not refer to any images in the main paper, but only to supplementary figures.\nI think if the authors want to dicuss this data they should put some in the main figures.\n7. Fig 5G, write on these panels that the red refers to ZO-1, as written on Fig 5C. Makes it clearer to reader."}, "author_response": "We genuinely thank Reviewer #1 for the thoughtful and encouraging evaluation of our study. We\nappreciate his.her positive feedback on our deep Dual RNA-Seq approach, as well as the relevance of\nour findings for the field of leptospirosis and beyond.\nMinor weaknesses:\n1. “Minor weaknesses include not using blood as a sample in addition to liver and kidney, …”\nResponse. We fully agree with Reviewer #1 that including blood in the Dual-RNASeq analysis in addition\nto liver and kidney would have been interesting and relevant for leptospiral infection. However, the\naim of this study was to focus on liver and kidney tissues to decipher paracellular transmigration\nmechanisms for bacterial dissemination and tissue colonization. We acknowledge though that blood\nanalysis would be instrumental to determine the host immune and inflammatory responses upon\nleptospirosis at the early phase of the infection and this will be investigated in future studies. This point\nis now discussed as a limitation of the study (lines 455-459) (see point B-3).\n2. “…and not including data on validation of the transcriptional identified host proteins”\nResponse. We have now included RT-qPCR analyses of selected host DEGs related to inflammation,\ncalcium signaling and actin/cell junction pathways of liver and kidneys (Supplementary Fig. 2E-F). This\nanalysis is described in the Results section (lines 145-148, and 248) and the legend of Supplementary\nFig. 2 was modified accordingly (Supplementary Information, lines 41-46).\nComments for the authors:\nA) On sequencing, Sequana pipeline, built on Snakemake, provides reproducibility and traceability;\ndetailed tools listed are standard and accepted in the field.\n1) The authors mention using “custom primer design on rRNA” with the RiboZero Plus kit. As this is a\nkey step in ensuring adequate depletion of Leptospira rRNA, a clarification if these were custom-\ndesigned capture probes specific to Leptospira rRNA (e.g., 16S, 23S), and whether hamster rRNA was\n1\nfully targeted by the standard probe set could be beneficial. Additionally, was the efficiency of rRNA\ndepletion for both species evaluated (e.g., Bioanalyzer, % rRNA reads)?\nResponse. We used custom-designed capture probes specific to Leptospira 16S and 23S rRNA for rRNA\ndepletion. These were generated using Sequana 0.180 Ribodesigner, an open-source tool developed\nin-house (https://github.com/sequana/sequana; Cokelaer et al. (2017) DOI: 10.21105/joss.00352). The\nprobes were designed using the default parameters and based on the rRNA sequences in the\nLeptospira reference genome (NZ_CP011931.1). Briefly, Sequana Ribodesigner determines the length\nof the probes and the spacing between them according to the input rRNA sequences and it returns a\nprobe set optimized for ribosomal depletion. For the host, the standard probes included in the Illumina\nStranded Total RNA Prep with Ribo-Zero kit are a pool of oligos targeting mitochondrial rRNA (human,\nmouse, and rat); no details are provided by the manufacturer about the sequences.\nWe assessed rRNA depletion efficiency for both Leptospira and hamster samples using preliminary\nsequencing (Shallow Sequencing). Based on the preliminary sequencing results, we found that around\n5% of rRNA remained among the Leptospira samples, and between 1 and 5% of rRNA among the\nhamster samples, confirming efficient depletion of rRNA from both species. As suggested, this\ninformation is now included in the Methods section (lines 575-578).\n2) The authors report detecting 4.57% to 79.34% of Leptospira genes, but the criteria for “gene\ndetection” are not specified. Recommend a clarifying state on threshold used (e.g., ≥1 read, CPM > 1)\nto improve transparency and enable reproducibility of the transcriptome coverage claims. It would also\nbe of interest to include the percentage of total reads mapping to the bacterial genome before and\nafter deep sequencing.\nResponse. We thank the Reviewer for raising this important point. In our study, a gene was considered\nas “detected” only if it had at least one read mapped in each biological replicate. Meaning that genes\nwith zero reads in any of condition were excluded from the list of detected genes. We have now\nclarified this criterion in the legend of Fig. 1 (line 959) to improve transparency.\nAs recommended, we have modified Supplementary Fig. 1 to include the percentage of reads mapping\nto the Leptospira genome before and after deep sequencing. To provide a more comprehensive view\nof the sequencing output and underscore the relevance of the dataset of Leptospira transcriptome\nanalysis, we have also added the absolute number of reads mapped to the Leptospira genome before\nand after deep sequencing. In addition, the number of reads mapped to the hamster genome is also\nreported. The legend of Supplementary Fig. 1 was modified accordingly (Supplementary Information,\nlines 17-25).\n3) Dual RNA-seq introduces the possibility of cross-mapping between host and pathogen reads,\nparticularly in low-abundance pathogen RNA contexts. While the authors mapped reads to both\ngenomes using STAR and Bowtie2, the manuscript would benefit from clarification on whether steps\nwere taken to prevent or quantify cross-mapping artifacts — for example, through combined genome\nindexing or ambiguity filtering. This would further strengthen confidence in the assignment of reads to\nLeptospira or hamster origin.\nResponse. We thank the reviewer for this important methodology point. To assess potential cross-\nmapping between Leptospira and hamster reads, we had aligned the uninfected hamster RNA-Seq\nlibraries to the Leptospira genome (see Tables “D1-D3_Kidney_raw_counts.txt” and “D1-\nD3_Liver_raw_counts.txt” in the GEO deposit). Despite tens of millions of reads per sample, only a very\nsmall number of hits were observed, and these mapped exclusively to rRNA loci. No reads aligned to\nLeptospira protein-coding genes, confirming that cross-mapping artifacts were negligible. This control\nanalysis therefore supports the reliability of read assignment in the infected samples. The absence of\ncross mapping has been mentioned in the Methods section (lines 588-591).\nB) On Other Methods:\n2\n1. Animal Model: The use of 4-week-old hamsters and a high infectious dose for this species should be\nexplained in the context of disease modelling and its translational relevance.\nResponse. The use of weanling hamsters (3-4 weeks old) is standard in leptospirosis research because\nthey are highly susceptible to L. interrogans. This allows for robust disease modelling with consistent\noutcomes (Wunder et al. (2016) https://doi.org/10.1128/iai.00094-16; Gomes-Solecki et al. (2017)\nhttps://doi.org/10.3389/fimmu.2017.00058). Intraperitoneal injection of 106 to 108 of L. interrogans is\nroutinely used in this model to reliably induce acute and severe leptospirosis, that reproduces the\npathological and clinical features of human disease, including vascular damages, renal and hepatic\ninjuries. This approach has been validated across numerous studies to ensure translational relevance,\nsupporting vaccine efficacy assessments and mechanistic investigations (Wunder et al. (2021) DOI:\nhttps://doi.org/10.7554/eLife.64166). In this study, our rational to use the higher relevant dose (108\nleptospires) was to obtain the highest Leptospira burden in liver and kidneys (Wunder, et al. (2016)\nhttps://doi.org/10.1128/iai.00094-16) and maximize the recovery of Leptospira RNA for the dual RNA-\nSeq analysis. We have modified the manuscript to include the relevance of the experimental animal\nmodel used in this study for mimicking clinical features of acute human leptospirosis (lines 101-103).\n2. Serovar: Since L. interrogans serovar Manilae was used it would be interesting to know if the authors\nexpect differences in RNAseq if another L. interrogans serovar was used, what about another species?\nGiven the use of Manilae for infection, why was lung not processed for RNA-seq?\nResponse. We thank the Reviewer for raising an insightful question related to two important aspects\nin leptospirosis research: the potential variability between Leptospira serovars and species, and the\nrationale for organ selection in the RNA-seq design performed in this study.\nThe Leptospira serovar is determined by the rfb locus, which encodes the O-antigen of the\nlipopolysaccharide (LPS). This locus varies across serovars and likely contributes to host specificity,\nparticularly in reservoir hosts where infection is asymptomatic. In our study, we used L. interrogans\nserovar Manilae, which is well established to induce acute leptospirosis in hamsters, a susceptible\nmodel that reproduces human-like disease manifestations. If a different L. interrogans serovar had\nbeen used, provided that (i) the hamster remains a susceptible host, and (ii) the new strain encodes at\nleast the two VM proteins studied here, we would expect a broadly similar pattern for dissemination\nand bacterial gene expression. Thus, we hypothesize that if hamsters are still susceptible to the\nselected strain, the differences will be minimal. However, certain differences could arise, particularly\nin the host’s innate immune response, as this is known to be modulated by the LPS structure.\nImportantly, the impact may be more pronounced when comparing different species (e.g., L.\nborgpetersenii or L. kirschneri), as there may vary in terms of their tropism, virulence and host\nadaptation mechanisms. We fully agree that the use of other Leptospira species and serovars\nrepresents a valuable direction for future studies. This point is now addressed in the Discussion section\n(lines 497-505).\nRegarding the organ selection in our study, we focused on liver and kidney, as they are the primary\nsites of Leptospira colonization in the hamster model of acute infection. Both organs typically harbor\nthe highest bacterial loads and exhibit clear histopathological lesions during acute disease (Haake\n(2006) https://doi.org/10.1002/9780471729259.mc12e02s02; Wunder et al. (2016)\nhttps://doi.org/10.1128/iai.00094-16). While we acknowledge the lung as a relevant colonization site\nin leptospirosis associated with severe pulmonary haemorrhagic syndrome, given the technical\nconstraints of Dual RNA-seq for pathogen transcript recovery, we prioritized organs with the highest\nbacterial burden. Nonetheless, we fully agree that transcriptomic analysis of infected lung tissue\nremains of high interest and the use of other Leptospira species and serovars represent a valuable\ndirection for future studies. This points is now addressed in the Discussion section (lines 459-462).\n3. Transcriptomic Focus: incorporating blood transcriptomics could have added valuable insights.\nResponse. We fully acknowledge the importance of studying blood transcriptional response upon\ninfection by Leptospira and we agree that incorporating blood transcriptomics would offer valuable\ninsights into the early host-pathogen interactions during Leptospira infection. In fact, we recently\n3\ncontributed to a study describing the transcriptomic profiling of L. interrogans exposed to whole blood\nfrom hamster (Garcia et al. (2025) https://www.biorxiv.org/content/10.1101/2025.01.22.634353v1).\nA comprehensive Dual RNA-Seq analysis of blood upon infection by different Leptospira strains (e.g.,\ndifferent serovars) would be a powerful way to uncover both bacterial adaptation mechanisms and\nhost-specific immune responses during the critical early phase of infection. This point is now addressed\nin the Discussion section (lines 455-459) (see minor comments, point #1).\n4. In Vitro Infection Conditions: The manuscript lacks details regarding the infection protocol for L.\ninterrogans in vitro. Specifically, was the infection performed in complete media with serum? This could\naffect bacterial uptake and cellular responses.\nResponse. As described in the Methods section, infections were performed using complete cell culture\nmedium (Minimum Essential Medium Eagle, MEME) supplemented with 10% heat-inactivated foetal\nbovine serum and 2mM L-glutamine. L. interrogans strains were diluted directly in this medium prior\nto infection of HEK-293T cells. As verified in previous studies (Giraud-Gatineau et al. (2024) PLOS\nPathogen DOI: 10.1371/journal.ppat.1012161), the use of heat-inactivated serum does not impair L.\ninterrogans viability and, importantly, allow avoiding any serum effect on the host-Leptospira\nresponse. We believe that our infection setup reflects standard conditions that preserve both host cell\nphysiology and bacterial viability, allowing us to investigate interactions at the host-pathogen\ninterface. This point is now clarified in the Methods section (line 615).\n5. In vitro analysis using HEK293T cells which is kidney cell line: it would be interesting to see results in\na hepatocyte cell line (e.g.HEPG2) since the highest differences in vivo are seen in liver. Alternatively,\nthis could be added to study limitations\nResponse. We sincerely appreciate the Reviewer’s insightful suggestion. We have performed infection\nwith the human hepatocyte cell line HepG2 and we could show disruption of cell junction (ZO-1 and\npan-cadherin) upon infection with L. interrogans. These results are now presented in the\nSupplementary Fig. 6 and we have modified the manuscript (lines 228-231) and the Supplementary\nInformation file (lines 84-103) accordingly.\nWe have also included experimental evidence of disruption of cell-cell junctions in the hepatocyte\nHepG2 cells when incubated with the L. interrogans strains WT and mutants (LIMLP_11660, dcas9-\nLIMLP_11655 and LIMLP_11660+dcas9-LIMLP_11655) (lived leptospires or supernatants). These new\nresults are presented in Supplementary Fig. 13. The manuscript (lines 354-355) and Supplementary\nInformation file (lines 222-251) were modified accordingly.\n6. Calcium Signaling Assessment: The use of Fluo-8 dyes and microscopy to assess calcium signalling is\nlimited in quantitative power. Flow cytometry could have provided a more accurate and robust\nquantification. Also, control image quality is poor, making interpretation difficult.\nResponse. To provide a more accurate and robust quantification, as recommended, we have analyzed\ncalcium content by flow cytometry with the Fluo-8 AM dye. We could confirm the increased calcium\ncontent upon infection by L. interrogans, observed previously by microscopy. These data are now\npresented in Fig. 5C. In addition, a similar quantification is also provided for the infection of human\nepithelial cells with the different VM Leptospira mutant strains (lived bacteria or supernatants) in\nSupplementary Fig. 15. The manuscript (lines 278 and 377-382) and the legends of Fig. 5 (lines 1080-\n1083) and Supplementary Fig. 15 (Supplementary Information file, lines 265-273) were modified\naccordingly (see Reviewer #3, point 4).\n7. F-actin Visualization: The antibody-based F-actin staining displays high background and non-specific\nsignals, complicating interpretation of actin dynamics. Phalloidin staining, a widely used method in the\nfield, offers more reliable visualization of filamentous actin. From the confocal images, no clear changes\nin actin polymerization are evident. A reduced number of cells in infected groups complicates\ninterpretation based on cell-free area quantification. Regarding cofilin analysis, please clarify whether\nthe total and phosphorylated cofilin bands were obtained from the same blot.\nactin) visualization in our study (Actistain 555; PHDH1, from Tebu) is in fact a phalloidin conjugate, and\ntherefore, the staining method employed is indeed Phalloidin-based. We agree with the Reviewer that\nthis information was not clearly stated in the manuscript, and we have therefore modified the Methods\nsection to explicitly indicate that Phalloidin staining was used (line 631-632).\nWe understand Reviewer #1’s concern regarding the apparent lower number of cells in the image of\ninfected sample (Fig. 4A-B). We wish to stress out that L. interrogans does not impair HEK-293T cell\nproliferation or cell death under the condition used in our study, as verified by cell viability assay (MTT\nassay for cell proliferation and Annexin V-Propidium iodide markers for cell death, see point B-10). The\nobserved lower cell density in the images of infected samples is due to increased cell dispersion and\nloss of cell-cell contacts during infection, but not to a reduction in total cell number. Importantly, the\nmicroscope field of view and magnification were kept constant across all conditions, which may\ncontribute to the visual impression of fewer cells in infected samples. The cell-free area quantification\nis a metric used here not to infer cell death but to assess the disruption of cell-cell junctions and\nmonolayer integrity, which are hallmarks of barrier dysfunction. Since the number of cells remains\nconstant in both conditions and since the staining clearly delineates cell borders, we believe this\nquantification remains valid and informative.\nRegarding cofilin analysis, the total and phosphorylated cofilins were not detected on the same\nimmunoblot membrane, due to their very similar molecular weights (~19 kDa). As such, separate blots\nwith identical protein loads and processed under the same experimental conditions were used for\nthese detections. This point is now mentioned in the legend of Supplementary Fig. 5 (Supplementary\nInformation, lines 79-80) to clarify the experimental setup.\n8. RAC1 Signaling Interpretation: The study reports changes at the phosphorylated RAC1 level, typically\nassociated with enhanced actin polymerization. However, the manuscript claims actin disorganization,\nwhich appears contradictory. Please clarify, possibly by analyzing downstream effectors of RAC1 that\ninfluence actin dynamics.\nResponse. We thank Reviewer #1 for this insightful comment on the apparent discrepancy between\nRAC1 activation and the observed actin disorganization during infection by L. interrogans. Activated\nphosphorylated RAC1 is indeed well-characterized Rho-family GTPase that typically activates\ndownstream effectors such as WAVE complex, PAK kinases or cofilin, thereby promoting branched\nactin polymerization in cells (Spence & Soderling (2015) DOI: 10.1074/jbc.R115.655118; Sit &\nManser (2011) https://doi.org/10.1242/jcs.064964.). However, RAC1 was also shown to regulate membrane\nruffling, lamellipodia turnover, and dynamic cytoskeletal reorganization (Marston et al. (2019)\nhttps://doi.org/10.1073/pnas.1808830116), which depending on the context can manifest as actin\nrearrangement.\nIn our infection model, the elevated Rac1 phosphorylation likely reflects activation of Rac1 signaling\npathways in response to bacterial infection. However, this activation does not necessarily lead to\norganized actin structures, as Rac-1 mediated signaling can promote dynamic turnover of actin\nfilaments rather than stable actin structures (Marston et al. (2019)\nhttps://doi.org/10.1073/pnas.1808830116; Sun et al. (2007) DOI: 10.1083/jcb.200705122).\nWe also observed increased activation of the downstream effectors cofilin and WAVE-2\n(Supplementary Fig. 5B), confirming Rac1 pathway involvement and enhanced actin dynamics.\nHowever, despite this activation, cell-cell junctions remained disrupted, indicating that Rac1 alone is\ninsufficient to maintain cytoskeletal integrity.\nTherefore, L. interrogans may perturb additional regulatory pathways or express effectors that directly\ninterfere with actin-binding proteins, leading to actin reorganization and junctional disorganization.\n9. Adhesion Molecule Analysis: While discussing calcium signaling and epithelial barrier function, it is\nimportant to also examine adhesion molecules such as ICAM, VCAM, cadherins, and catenins. The\nauthors have cited relevant literature supporting their role in Leptospiral pathogenesis, but no data is\npresented. This could be tested and/or added as a study limitation.\nmaintaining epithelial integrity and, indeed, several of these factors have already been shown to\nparticipate in disruption of cell-cell junction associated with leptospirosis (H. Sato & J. Coburn (2017)\nhttps://doi.org/10.1371/journal.pntd.0005830; Sebastian et al. (2021) DOI: 10.1111/cmi.13343). Our\naim was to further characterize L. interrogans dissemination, focusing on unexplored molecular\npathways such as disruption of tight junction. Nevertheless, as suggested, we have assessed the effect\nof Leptospira on the integrity of cadherins. Using a pan-cadherin antibody, we could show a decrease\nof pan-cadherin content in the plasma membrane upon infection by Leptospira (presented now in Fig.\n4E). we could confirm that infection by L. interrogans compromises cadherin junction, as previously\nshown (H. Sato & J. Coburn (2017) https://doi.org/10.1371/journal.pntd.0005830). We also confirmed\nthat the activation of calmodulin-MLCK pathway through Ca2+ signaling partially restored pan-cadherin\nlocalization at cell-cell junctions (presented in Supplementary Fig. 9). The manuscript (lines 241-242,\n245-248, 303, 313, 671, and 962), the legend of Fig. 4 (lines 1064-1069) and the Supplementary\nInformation file (lines 146-160) were modified accordingly. In addition, we included the experimental\nevidence of the decrease of pan-cadherin in plasma membrane of cell-cell junctions in epithelial cells\nwhen incubated with the supernatants of or infected with different L. interrogans strains (WT and VM\nmutants) (presented in now Supplementary Fig. 12A). The manuscript (lines 370, 373-378), and the\nSupplementary Information files (lines 200-208) were modified accordingly.\n10. Cell Death Assessment: The authors report no evidence of epithelial cell death. However, no direct\nassays for cell death pathways (e.g., apoptosis, necrosis) were performed. The MTT assay, being a\ncolorimetric method for cell viability, cannot definitively rule out cell death. The authors should consider\nperforming more specific assays (e.g., Annexin V/PI staining, caspase activity, TUNEL assay) to\nsubstantiate cell death.\nResponse. We thank Reviewer #1 for this thoughtful comment on the limitation of MTT assay. While\nthe MTT assay suggests preserved cellular viability, we agree that this method does not distinguish\nbetween different modes of cell death. As suggested, we have performed Annexin V/PI staining to\ndirectly assess apoptosis and necrosis, thereby providing a more definitive evaluation of the survival\nof infected epithelial cells. We did not detect any apoptosis nor necrosis upon infection of HEK-293T\ncells (presented now in Fig. 4D) or HepG2 cells (presented now in Supplementary Fig. 6B). The\nMethods section (lines 666-670), the legends of Fig. 4 (lines 1011-1015) and Supplementary Fig. 6B\n(Supplementary Information, lines 86-92) have been modified.\nReviewer #2 (Remarks to the Author)\nGIRAUD-GATINEAU et al.\nIn this work, the authors, using dual RNA-Seq, determined the in vivo host-pathogen transcriptomic\nlandscape upon infection by the extracellular pathogen Leptospira interrogans. They found a novel\nmechanism of cell-cell junction disruption, where an increase in intracellular calcium triggered tight\njunction destabilization, by activating the calmodulin and myosin light chain kinase signalization.\nAdditionally, two novel bacterial effectors of the Virulence-Modifying (VM) proteins family, structurally\nrelated to toxin-like proteins, were found. These proteins promoted modulation of calcium homeostasis\nand disruption of cell-cell junctions, thereby allowing Leptospira translocation across epithelium\nbarriers, tissue colonization and pathogenicity. This is a very interesting and innovative study. The high-\nquality experimental data support the authors' conclusions.\nWe would like to thank Reviewer #2 for his/her very positive evaluation of our study. We really\nappreciate that our findings were acknowledged for their novelty, innovation, significance, and quality.\nReviewer #3 (Remarks to the Author)\ndual RNAseq approach. They find an increase in calcium signalling and associated changes in tight\njunction destabilization and identified two VM protein family effectors that that are linked to cell\njunction disruption. Overall, the paper is well written and data well presented. The finding that the VM\neffectors are associated with disruption of cell junctions is quite interesting. However, in general the\ndata supporting some of the findings, for example, the mechanisms underlying calcium signalling, are\nnot that convincing. Calcium signalling induced by other pathogens like Pseudomonas leads to\ndisruption of epithelial junctions, so this mechanism is not unique to Leptospira.\nMy specific concerns with the study are\nWe would like to thank Reviewer #3 for his.her thoughtful and pertinent suggestions and reviews.\n1. Introduction: The authors claim in line 34 ‘the mechanisms employed by extracellular pathogens\nremain comparatively underexplored’ and in line 44 that molecular mechanisms of cell-cell junction\ndisruption are not understood. This is not true as there are numerous examples of virulence\nmechanisms at host-pathogen interfaces of extracellular pathogens like staphylococcus, pseudomonas,\nclostridium etc. There are several bacterial toxins including staphylococcal, clostridial toxins that have\nbeen shown to disrupt epithelial barriers.\nResponse. We agree with the reviewer that some mechanisms of infection by extracellular pathogens,\nincluding mechanisms of epithelial barriers disruption, have already been characterized. We realized\nthat the aforementioned statement could be interpretated as overstated, and we have modified\nconsequently the introduction to acknowledge previous studies on extracellular pathogen-induced\njunction disruption, particularly by specific toxins (lines 39-40 and 49-52).\nWe would like to emphasize though that while mechanisms of cell-cell junction disruption are\nestablished for some pathogens, mechanisms for understudied bacteria, which do not encode known\nvirulence factors are less understood. By demonstrating a role of Leptospira VM protein family\neffectors, structurally distinct from previously characterized toxins, in promoting cell-cell junction\ndisruption and modulating host calcium signaling, the present study will significantly advance the field\nof infection by extracellular pathogens.\n2. Would be good to know the numbers of sequencing reads obtained from bacterial and the hamster\nhost\nResponse. A recommended, we have indicated the number of reads mapping in Leptospira genome\nbefore and after deep sequencing, and the number of reads mapped to the hamster genome (see\nSupplementary Fig. 1). The legend of Supplementary Fig. 1 was modified accordingly (Supplementary\nInformation, lines 17-25) (see also Reviewer #1, point A-2).\n3. Figure 4 essentially demonstrates that actin reorganisation and epithelial cell junction disruption is\nseen in human HEK293T cells. This section essentially is reporting what has already been previously\nreported in the literature.\nResponse. We fully agree with the Reviewer that the disruption of epithelial and endothelial cell\njunctions and actin reorganization upon L. interrogans infection have been previously reported in the\nliterature. In fact, we have acknowledged these previous studies (H. Sato & J. Coburn (2017)\nhttps://doi.org/10.1371/journal.pntd.0005830; Sebastian et al. (2021) DOI: 10.1111/cmi.13343). Our\npurpose was not to re-establish these known phenomena, but rather to use them as cellular readout\nfor validating the dual RNA-Seq data and the functional investigation of the two newly identified VM\nproteins. Indeed, dual RNA-Seq with an extracellular pathogen in vivo is an approach which is still rare\nin the field. Data presented in Fig. 4 essentially serve to demonstrate that the experimental set up used\nin the study recapitulates key aspects of cell-cell junction disruption associated with leptospiral\ninfection.\n7\n4. Figure 5- Could the authors quantify fluorescence over time with the Fluo-8 AM staining assay? This\nwould provide a non-microscopy-based quantification of intracellular calcium.\nResponse. As recommended, we have now included the quantification of Fluo-8 AM fluorescence\nintensity by flow cytometry to provide a non-microscopy-based quantification of intracellular calcium.\nWe could confirm the increase of calcium content upon infection by L. interrogans previously observed\nby microscopy. These data are now presented in Fig. 5C. In addition, a similar quantification is also\nprovided for the infection experiments of human epithelial cells with the different VM Leptospira\nmutant strains (lived bacteria or supernatants) in Supplementary Fig. 15. The manuscript (lines 278\nand 377-382) and the legends of Fig. 5 (lines 1080-1083) and Supplementary Fig. 15 (Supplementary\nInformation file, lines 265-273) were modified accordingly (see also Reviewer#1, point B-6).\n5. Please include quantification of band intensities from blots showing the phosphorylation of MCL2\nblots/ normalised to MCL2 and the loading control. Data should typically be from at least three blots.\nResponse. As suggested, we have provided now the quantification of the band intensities\ncorresponding to total and phosphorylated MCL2 performed in triplicate (see Fig. 5G). The legend of\nFig. 5 has been modified accordingly (lines 1106-1109).\n6. Most of the conclusions made on inhibitor effects depend on the ZO-1 staining and cell-free area\nquantification, which are both microscopy-based. Were these microscopy quantifications performed\nblinded? Immunoblotting ZO-1 or quantifying another marker would help strengthen these data.\nResponse. We thank the reviewer for this important and challenging remark. We acknowledge that\nmicroscopy-based quantifications of ZO-1 and cell-free areas were not conducted in a blinded manner.\nThis limitation is now clearly disclosed in the Methods section (lines 635-636) to maintain full\ntransparency.\nTo strengthen the reliability of the data obtained with ZO protein staining and inhibitors, and upon\nsuggestions of Reviewer #1 (see points B-5 and B-9), we obtained the following additional results. We\nobtained the evidence that (i) infection by L. interrogans leads to the disruption of cadherin in human\nepithelial cells and treatment with inhibitors (BAPTA-AM, KN-83 and ML-7) reduces this disruption (Fig.\n4E, Supplementary Fig. 9), (ii) cadherin amounts at the cell-cell junction are reduced when epithelial\ncells are incubated with supernatant of WT L. interrogans culture (Supplementary Fig. 12), (iii)\nreduction of both ZO-1 and cadherin upon infection by L. interrogans is also observed in infected\nhuman HepG2 hepatocytes (Supplementary Fig. 6), and (iv) VM proteins participate in the reduction\nof ZO-1 and cadherin amounts at the cell-cell junction, as demonstrated using supernatants from VM\nmutants cultures (LIMLP_11660, dcas9-LIMLP-11655 and LIMLP-11660+dcas9-LIMLP_11655) in both\nepithelial cells and human HepG2 hepatocytes (Supplementary Fig. 12 and 13). In conclusion, the\nsignificant role of VM effectors in cell-cell junction disruption and modulation of calcium signaling can\nbe observed with different markers (ZO-1 and cadherin) and cell lines. Together with our finding that\na defect of translocation across human epithelial cells can be observed using supernatant of L.\ninterrogans mutant (Fig. 6C), these results provide robust and convergent evidence supporting a\nmechanistic link between VM effectors and cell junction integrity.\n7. The differences in the %leptospires recovered after translocation is lower, however the difference is\nquite nominal, and it is not clear if the differences are statistically significant.\nResponse. We fully agree with the Reviewer’s observation on the relatively low percentage of L.\ninterrogans translocation observed in transwell assay experiments. It is important to note that\nconsiderable variations in the translocation rates of L. interrogans are reported in the literature,\ndepending on the experimental model used, ranging from approximately 4-8% (Vieira et al. (2013)\nDOI: 10.1128/IAI.00020-13; Zhu et al (2023) https://doi.org/10.1371/journal.ppat.1011313) up to 60%\n(Martinez-Lopez et al. (2010) https://doi.org/10.1371/journal.pntd.0000918). This variability likely\nreflects complex factors, such as fluid dynamics, host cell types and microenvironmental conditions,\ninfluencing L. interrogans dissemination. The transwell assay, while useful, exhibits low efficiency and\nhas inherent limitations. The use of negative controls is essential in this assay. In the present study, we\n8\nhave used the saprophytic species L. biflexa that consistently displayed a significantly lower\ntranslocation rate at different time points compared to L. interrogans (see Fig. 4L) as negative control.\nIn addition, VM mutant strains exhibited a lower translocation ability (Fig. 6B) and VM proteins in the\nsupernatant of L interrogans culture increases the ability of L. biflexa to translocate epithelial barriers\nas demonstrated in Fig. 6C using WT and VM mutant strains. This validates the specificity of L.\ninterrogans translocation capacity and supports the presence of dedicated mechanisms employed by\nthis pathogenic species to cross cellular barriers. Although the absolute translocation rates remain low,\naltogether these findings strengthen the biological relevance of the transwell assays presented in the\npresent study (see also Reviewer #4, Major Concern point#1).\n8. Figure 6. It is nice to see that the LIMLP_11660 mutant supernatants are affecting translocation,\nand ZO-1 staining. Do whole WT and mutant bacteria incubated with the cells also have similar effects?\nAnd also, is there an increased calcium signalling seen with whole bacteria.\nResponse. We thank the reviewer for this important question. Analyses of cell junction integrity (as\nassessed by ZO-1 and cadherin stainings) upon direct infection of epithelial HEK293T and hepatocyte\nHepG2 cells with whole L. interrogans WT and VM mutant strains are now included in the revised\nmanuscript (Supplementary Fig. 12B-C, 13A and 13C). Junction disruption is equally observed with\nwhole L. interrogans WT and most mutant strains, but cell-cell junction disruption is partially reduced\nonly when both LIMLP_11660 and LIMLP_11655 are concomitantly inactivated or silenced (mutant\nLIMLP_11660+dcas9-LIMLP_11655). This demonstrates that both VM proteins act cooperatively to\npromote efficient junction disruption during infection. The manuscript (lines 351-355), the legend of\nSupplementary Fig. 12 (Supplementary Information file, lines 209-220) and Supplementary Fig. 13\n(Supplementary Information file, lines 224-231 & 240-245) have been modified.\nRegarding intracellular calcium concentrations during infection, we observe a similar increase in calcium\ncontent when infecting epithelial cells with whole L. interrogans or when incubating cells with\nsupernatant of L. interrogans culture. These data are now presented in Supplementary Fig. 15. The\nmanuscript (line 382) and the legend of Supplementary Fig. 15 have been updated (Supplementary\nInformation, lines 265-273).\nInterestingly, the impact of inactivating VM genes on cell-cell disruption (as assessed by ZO-1 and\ncadherin staining) and calcium signaling is more pronounced with supernatants than with whole\nbacteria. This indicates that L. interrogans possesses additional factors, beyond secreted VM proteins,\nsuch as adhesins or other surface molecules, that contribute to the disruption of cell junctions and\nmodulate intracellular calcium levels directly or indirectly.\n9. Line 256, states ‘infection’ with mutants, although the figure legend for 6G says supernatants\nwere added to cells- please clarify.\nResponse. We thank the reviewer for pointing out this mistake. We have corrected it accordingly\n(lines 361, 371, 378-379, and 382).\n10. I think it is possible that the proteins are bound to receptors on the cell surface- I don't think the\ndata shown support the conclusion that the FLAG tagged protein is internalised. Have the authors tried\nto do immunofluorescence with anti-FLAG to study protein localisation in the host cell?\nResponse. We do appreciate the reviewer’s comment which helps improving the clarity and accuracy\nof our conclusion. Reviewer #3 is totally right to consider that our data do not conclusively\ndemonstrate internalization of the VM proteins inside host cells. In fact, we have attempted to detect\nthe flagged VM protein inside the host cells using anti-3xFLAG antibodies. Unfortunately, these\nexperiments did not yield detectable fluorescence of VM despite using the same antibody that\nsuccessfully detects VM in immunoblots. The lack of immunofluorescence signal could be explained by\na limited accessibility of the FLAG epitope into the cellular context, an insufficient antibody penetration\nin fixed cells, and/or a too low abundance of the internalized protein. We have accordingly revised\nsome of the conclusions in the manuscript to reflect these considerations and limitations. We have\nstated that VM (i) is secreted by L. interrogans, and (ii) interacts with and modulates the host cell\n9\nenvironment (lines 28 (abstract) and lines 372-373), rather than being definitively internalized. The\ntitle of Fig. 6 was also modified (lines 1154-1155).\n11. Methodology: No details were provided regarding the quantification of images- how many cells,\nfields were counted? Icy software was mentioned, no version and no details on what was quantified-\nfor e.g., cell free area, how was this defined etc\nResponse. We thank the reviewer for raising this point. For each experiment, a minimum of 3 random\nfields per condition and at least 100 cells were analyzed to ensure representative sampling. A more\ndetailed description of the quantification and imaging protocol (number of fields and cells analyzed,\ndefining regions of interest, the version of Icy software) has now been included in the Methods section\n(lines 647-659).\n12. Statistics- unpaired t tests have been used in many figures, e.g. figure 1 B where there are multiple\ndata sets being compared-I believe an ANOVA or similar should be used here.\nResponse. We thank the reviewer for this important comment regarding the statistical analyses. We\nprovide now analyses by One-way ANOVA test when the unpaired t test was not relevant. The legends\nof Fig. 1B, Fig. 5B, Fig. 5E-F, Fig. 5I-J, Fig. 6B-D, and Fig. 6G have been modified accordingly.\nReviewer #4 (Remarks to the Author)\nThis is a beautiful paper that uses dual RNAseq to identify bacterial and host pathways altered during\nin vivo infection (VM proteins and calcium signalling/actin cytoskeleton) and culminates with\ndemonstrating a mechanistic link between the two. Whilst it does not yet show exactly what the\nmolecular basis is – what is the cellular target of the two VM proteins and how do they affect calcium\nsignalling/cytoskeleton? – it is my opinion that these first findings presented here are substantial\nenough in scope to warrant publication in Nature Communications.\nWe are grateful to Reviewer #4 for his.her appreciative and positive assessment of our study and\nrecognising the importance of our findings.\nMajor(ish) concerns:\n1. I am not convinced by the transwell assays used in Fig. 4L, 4J and 6B. As performed, it cannot\ndefinitely say that the increase in bacteria in the lower chamber is due to migrated bacteria, rather\nthan an increase in bacterial growth through replication occurring over this time period. A better assay\nwould be to infect the cell layers with the bacteria as done here, but then test for passage of an\nunrelated, inert particle such as FITC-dextran, a protein, or an inactivated bacterium. This will\nseparate out permeability from cell growth. The version used in 6C is OK because this measures transfer\nof L. biflexa in monolayers infected with L. interrogans.\nResponse. We thank the reviewer for raising this important point regarding the relevance and\nlimitations of the transwell assays presented in the present study. We fully agree with the importance\nof distinguishing active migration across epithelial barriers from bacterial replication. In our\nexperimental system, however, it is very unlikely that bacterial multiplication explains the observed\nincrease in bacteria number in the lower chamber as L. interrogans do not replicate in EMEM medium\nduring the timeframe of the transwell assays, although they remain viable. To further address this\nconcern, we performed an experiment with a non-motile L. interrogans flaA2 mutant, shown to grow\nat a rate comparable to that of the WT strain. As shown in the Reviewer only Figure below, under\nidentical conditions, the flaA2 mutant did not cross epithelial monolayers, in contrast to the WT strain\n(see panel B below). This strongly supports that the increased number of bacteria recovered in the\nlower chamber is mainly due to active transmigration rather than to bacterial growth.\n10\n2. There is not enough information about the in vitro sample to assess whether this is an appropriate\ncontrol, and this is important as it forms the basis of all the comparative dualRNAseq analysis on\nbacterial transcripts. Was this isolated at exponential growth or stationary growth? Also, is it known\n(from the literature) how many of these genes differ at different stages of in vitro growth? i.e. which of\nthese genes are generally variable to growth conditions, compared with those specifically altered\nduring in vivo infection?\nResponse. We thank the reviewer for raising this important point about the relevance of in vitro-\ncultivated Leptospira as control for the transcriptional response in vivo. We agree that in\ntranscriptomic studies under in vivo conditions, one key question is the suitability of the baseline\ncomparison. For the control, L. interrogans were cultivated at 30°C in the classical EMJH medium for\nLeptospira spp. and harvested at exponential phase (OD » 0.3; 109 leptospires/mL) and RNAs were\n420\nextracted from 1010 leptospires. This information has been indicated in the Methods section (line 569).\nThe shift from the EMJH culture medium to the host tissues corresponds to dramatic, complex and\npleiotropic environmental changes, having a strong and significant influences on gene expression in\nbacteria. Transcriptomes of Leptospira upon exposure to the host temperature (37°C) (Lo et al. (2006)\nhttps://doi.org/10.1128/iai.00755-06), the host osmolarity (Matsunaga et al. (2007)\nhttps://doi.org/10.1128/iai.01619-06), or to serum (Xue et al. (2010)\nDOI: 10.1371/journal.pntd.0000857) allow inferring to some extent what are the specific host-related\nsignal that influence the expression of a particular gene. Unfortunately, no transcriptome of Leptospira\nat different stages of the in vitro growth was published to date.\nNevertheless, we believe that the comparison with in vitro-cultivated and exponentially growing\nLeptospira remains relevant and useful in the context of transcriptomic studies on Leptospira field.\nThus, differential gene expression in vivo reflects adaptation to the complex and pleiotropic infectious\nenvironment, highlighting processes linked to pathogenicity.\nMinor comments:\n11\n1. Line 86, at a first read it sounds like these percentages refer to the percentage of total in vivo reads\nthat are bacterial. After reading more carefully it became apparent that this refers to the percentage\nof total genes encoded in the bacterial genome that are expressed. It would be good to rephrase this\nto make it clearer to understand. Also it should be 4.57 not 4,57.\nResponse. As recommended, we have changed the sentence and stated that the percentages referred\nhere correspond to the fraction of all Leptospira genes encoded in the genome that were detected as\nexpressed, not to the proportion of bacterial reads in the total sample. We have also corrected 4,57\ninto 4.57 (lines 115-116).\n2. Fig 2 – confusing to use red/blue for different organs in Fig 2C, as everywhere else it refers to\nup/down regulated.\nResponse. We have modified colors of Fig. 2C to avoid confusing with red and blue of up- and down-\nregulated genes, respectively. Kidney and liver samples correspond now to brown and green,\nrespectively. The legend of Fig. 2 was modified accordingly (line 978).\n3. Fig 3C, it is not clear why these particular “poorly characterised” genes were selected for display\nhere. Highest fold change? Most interesting?\nResponse. Around 30% of L. interrogans genome encodes for hypothetical proteins. We have chosen\nto present the most up- and down-regulated poorly characterized and hypothetical genes as this\ninformation might be important for future studies. In fact, the two VM proteins characterized in the\npresent study are annotated as hypothetical proteins as well.\n4. Line 153 and Fig 4A, talk about actin rearrangement but this cannot be seen (at least to my eye) in\nthe panel it refers to.\nResponse. We agree with the reviewer that the effect visible in Fig. 4A is more related to changes in\noverall cell morphology than to a clear actin rearrangement. However, in infected cells, we observe\nthat the cytoskeleton appears less expanded compared to uninfected cells. While this does not show\na classical reorganization of actin filaments, the actin staining seems more condensed around the\nnucleus with reduced intercellular contacts, in contrast to the more spread morphology of uninfected\ncells. We have revised the manuscript accordingly to better reflect these observations (lines 208-209).\n5. Fig 4H – is there a reason why JAM-1 not included here?\nResponse. JAM-1 was included in the confocal analysis (Fig. 4F-G) to assess junctional organization\nupon L. interrogans infection; however, the mechanistic validation in this study focused on the ZO\nfamily of tight junction proteins. ZO proteins are key scaffolding components that link transmembrane\nproteins to the actin cytoskeleton and play a central role in maintaining barrier integrity. In our study,\nthese proteins showed the most pronounced alterations upon L. interrogans infection and were\ntherefore prioritized for protein validation by western blot. While JAM-1 provided complementary\nlocalization data by confocal microscopy regarding cell-cell junction disruption, our mechanistic\ninvestigations were focused on ZO proteins and cadherins (in the revisited manuscript).\n6. Lines 176-191 – this substantial section does not refer to any images in the main paper, but only to\nsupplementary figures. I think if the authors want to dicuss this data they should put some in the main\nfigures.\nResponse. Findings presented in this study indicate that infection by L. interrogans modulates the ER\nactivity, but we could not establish its participation in the cell-cell junction disruption mechanism.\nTherefore, we presented these data as the Supplementary Figures. However, we agree that the section\ndescribing ER activity on infected cells might be seen as lengthy compared to its importance in the\nmanuscript. We have now shortened this section (lines 242-255).\n12\n7. Fig 5G, write on these panels that the red refers to ZO-1, as written on Fig 5C. Makes it clearer to\nreader.\nResponse. We thank the reviewer for this thoughtful suggestion. We have now added colors legend in\npanel of Fig. 4B, Fig. 4I, Fig. 5A, Fig. 5H (Fig. 5G in the previous version of the manuscript), Fig. 6D and\nFig. 6G.\n“In vivo Dual RNA-Seq uncovers key effectors of epithelial barrier disruption by an\nextracellular pathogen” Giraud-Gatineau et al.\nPoint-by-point response to the Reviewers\nWe sincerely thank all the Reviewers for their previous helpful and constructive\nfeedback. We are pleased that they are satisfied with the revised version.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this revised manuscript the authors addressed my previous comments and concerns\nto the original submission.\nWe are pleased that Reviewer #1 finds that her.his concerns have been appropriately\naddressed.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed most of my comments satisfactorily.\nWe are pleased that Reviewer #3 finds that most of her.his concerns have been\nappropriately addressed.\nI however noticed that the statistical information provided is not in line with the journal\nrequirements (eg exact p values, test details).\nResponse. We thank the Reviewer for this remark. We have now provided all the\nnecessary detailed information about the statistic tests (nature of tests, sample size,\nnature of replicates, p-values, etc.) performed in this study in the Figure legends and\nin the Source Data file (for Fig. Supplementary 2). This now complied with the editorial\nrequirements of the journal.\npage 1 line 42 -change to occludin\nResponse. We thank the Reviewer for pointing out this mistake. We have corrected\n“occluding” into “occludin” (Line 44, word highlighted in yellow).\nReviewer #4 (Remarks to the Author):\nAuthors have addressed all my concerns\nResponse. We are pleased that Reviewer #4 finds that her.his concerns have been\nappropriately addressed."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised manuscript the authors addressed my previous comments and concerns to the original submission.", "reviewer_3": "The authors have addressed most of my comments satisfactorily. I however noticed that the statistical information provided is not in line with the journal requirements ( eg exact p values, test details).\npage 1 line 42 -change to occludin", "reviewer_4": "Authors have addressed all my concerns"}, "author_response": "We sincerely thank all the Reviewers for their previous helpful and constructive\nfeedback. We are pleased that they are satisfied with the revised version.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this revised manuscript the authors addressed my previous comments and concerns\nto the original submission.\nWe are pleased that Reviewer #1 finds that her.his concerns have been appropriately\naddressed.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed most of my comments satisfactorily.\nWe are pleased that Reviewer #3 finds that most of her.his concerns have been\nappropriately addressed.\nI however noticed that the statistical information provided is not in line with the journal\nrequirements (eg exact p values, test details).\nResponse. We thank the Reviewer for this remark. We have now provided all the\nnecessary detailed information about the statistic tests (nature of tests, sample size,\nnature of replicates, p-values, etc.) performed in this study in the Figure legends and\nin the Source Data file (for Fig. Supplementary 2). This now complied with the editorial\nrequirements of the journal.\npage 1 line 42 -change to occludin\nResponse. We thank the Reviewer for pointing out this mistake. We have corrected\n“occluding” into “occludin” (Line 44, word highlighted in yellow).\nReviewer #4 (Remarks to the Author):\nAuthors have addressed all my concerns\nResponse. We are pleased that Reviewer #4 finds that her.his concerns have been\nappropriately addressed."}]

Decision letter