Graphical abstract
graphic file with name fx1.jpg
[39]Open in a new tab
Highlights
* •
Early exposure to Borrelia decreases endothelial cell motility and
physical forces
* •
Early exposure to Borrelia also upregulates the host’s innate
immune signaling pathways
* •
Host cell mechanics and signaling return to steady state at late
exposure times
* •
Exposure to dead bacteria steadily reduces motility and physical
forces of host cells
__________________________________________________________________
Immunology; Microbiology; Cell biology; Biophysics; Transcriptomics
Introduction
Lyme disease is a multisystem infectious disease and the most common
and rapidly growing tick-borne infection in the northern hemisphere
([40]Feria-Arroyo et al., 2014; [41]Meriläinen et al., 2015). It is
caused by a group of bacteria that belong to the Borrelia burgdorferi
(sensu lato) complex, comprised more than 20 genospecies ([42]Shan
et al., 2021). In the majority of infected individuals, prompt
treatment with antibiotics is sufficient to resolve the infection
([43]CDC Lyme Disease). However, a subset of infected patients does not
return to health and experiences symptoms including neurologic
impairment, fatigue, cardiac and arthritic hallmarks as well as a
progressive atrophy of the skin – which persist for over 6 months after
antibiotic treatment. Although the cause of these continued symptoms is
unknown, there is evidence of the continued presence of Bb antigens and
ongoing inflammatory responses ([44]Jutras et al., 2019; [45]Rouse
et al., 2021).
Infected ticks transmit Bb into the dermal interstitial fluid while
feeding, and from there the pathogens can disseminate throughout the
body ([46]Coburn et al., 2021). A key step in bacterial dissemination
is the ability of Bb to bypass the endothelial cell (EC) lining of
blood vessels. To maintain their barrier function ECs form protein
complexes at their surfaces that allow them to strongly anchor to the
extracellular matrix (ECM) through focal adhesions and to each other
through cell-cell junction complexes. The actin cytoskeleton connects
these anchors to each other forming a network that allows the cells to
transmit forces to their ECM (traction forces) and to each other
(intercellular forces).
Various advances in biomechanical techniques have recently enabled the
characterization of both the kinematics and dynamics of the motion of
cells in monolayer in health and disease. For example, traction force
microscopy (TFM) allows direct measurement of the traction forces
generated by confluent ECs on their matrix through the active
engagement of focal (cell-ECM) adhesions and actomyosin contractility
([47]Rokhzan et al., 2019). An additional technique, monolayer stress
microscopy (MSM) enables indirect calculation of the forces that cells
in monolayer exert on one another, which is critical in revealing how
cells regulate intercellular communication and barrier integrity
([48]Bastounis et al., 2022; [49]Bazellières et al., 2015). Calculation
of cell-generated forces has also provided key insight into how certain
intracellular bacterial pathogens modulate host cell forces to their
own benefit, that is, to disseminate better through tissues
([50]Faralla et al., 2018; [51]Lamason et al., 2016). For example,
intracellular Listeria monocytogenes hijacks host epithelial cell
mechanotransduction weakening the traction stresses that host
epithelial cells exert on their ECM, so that it can more efficiently
transmigrate through the cell ventral surface and into the underlying
basement membrane ([52]Faralla et al., 2018). Although Bb is
traditionally considered an extracellular pathogen, studies have shown
that it can also get internalized within multiple cell types including
ECs ([53]Wu et al., 2011; [54]Ma et al., 1991; [55]Livengood and
Gilmore, 2006; [56]Larsen et al., 2003; [57]Girschick et al., 1996),
suggesting that Bb infection may be able to induce similar alterations
in host EC biomechanics.
Of interest, gene expression analysis of host cells, including
epithelial and endothelial cells, infected with Bb in vitro has
revealed that many of the pathways that are related to the regulation
of the actin cytoskeleton and focal adhesions show differential
expression during the early and late stages of infection ([58]LaFrance
et al., 2011). Various integral components of host cell focal
adhesions, like the integrin heterodimers α5β1 and αvβ3, facilitate
adhesion of Bb to host cells ([59]Coburn et al., 1998). They also
mediate endothelial cell migration and mechanotransduction
([60]Bastounis et al., 2019; [61]Reinhart-King et al., 2005),
suggesting that by interfering with integrin signaling Bb may alter
host cell mechanics. In addition, exposure of different host cell types
to Bb activates innate immune signaling pathways such as those
dependent on the transcription factor NF-κB ([62]Dev et al., 2011). Of
interest, we recently showed that activation of innate immune
signaling, particularly of NF-κB, by infection can lead to dramatic
changes in host cell mechanics including a decrease in traction forces
of infected as compared to uninfected cells ([63]Bastounis et al.,
2021). In the context of Bb infection, a weakening in intercellular
force transduction could enhance paracellular permeability favoring
bacterial transmigration through the endothelium.
Tick-transmitted Bb typically enters into the host bloodstream as
single spirochetes whose diameter of 200 nm may be critical for
transmigration through the endothelium and other tissues
([64]Meriläinen et al., 2015; [65]Burns et al., 1997). Bb can also
localize intracellularly in many host cell types, including ECs, from
which it can escape clearance and transmigrate transcellularly
([66]Coleman et al., 1995; [67]Tan et al., 2021; [68]Kumar et al.,
2015). Moreover, Bb can undergo different morphological transitions,
forming for example spherical round bodies and aggregates ([69]Anderson
and Brissette, 2021; [70]Sapi et al., 2012). Whether these alternative
Bb forms emerge as a result of the harsh in vitro conditions, the
multiplicity of infection, or represent a mechanism to evade in vivo
host immune responses remains controversial ([71]Murgia and Cinco,
2004; [72]Anderson et al., 2003).
To determine whether exposure of ECs to Bb alters their biomechanics,
we used video-microscopy to monitor EC monolayers for up to two days
after exposure to Bb. We discovered a sharp and transient increase in
EC traction forces and intercellular forces exerted on neighboring ECs,
followed by a prolonged decrease in EC motility and physical forces up
to 15 h after exposure (hpe). But all variables returned to levels
similar to those of unexposed ECs at a later stage (24 hpe).
Concomitantly, RNA sequencing analysis of Bb-exposed or unexposed ECs
revealed that multiple innate immune signaling pathways (e.g., NF-κB,
TNFα) were significantly upregulated at early but not late exposure. Of
interest, we found that exposure of ECs to heat-inactivated Bb
recapitulates the early weakening of EC mechanotransduction but not the
reversal observed at late exposure. Altogether our findings suggest a
tightly regulated interplay between innate immune signaling and
physical forces in host ECs and a differential modulation of those
processes at early versus late exposure to Bb.
Results
Bb are internally localized within ECs in a dosage-dependent manner at early
exposure
To infer whether exposure of ECs to Bb alters host cell mechanics, we
first sought to evaluate how exactly Bb interacts with ECs and whether
bacteria are localized extracellularly or also intracellularly. To this
end, we grew Bb constitutively expressing GFP (Bb-GFP) in BSK-H media
to mid-log phase ([73]Dunham-Ems et al., 2009) and confirmed via
microscopy that all Bb retained single spirochete morphology and
expressed GFP ([74]Figure 1A and [75]Video S1). To quantify
internalization efficiency of Bb into ECs, we exposed monolayers of
human microvascular endothelial cells −1 (HMEC-1) to Bb-GFP for 4hat
different multiplicities of infection (MOI). We used HMEC-1 as model
host cells because they are dermal microvascular ECs and therefore
probably the first type of ECs Bb interacts with after skin infection
through a tick bite. In addition, this cell line has been previously
used to interrogate interactions of Bb with ECs ([76]LaFrance et al.,
2011). Following extensive washing, we trypsinized the cell monolayers
and determined the fraction of ECs that were GFP-positive in each well
using flow cytometry ([77]Figures 1B and 1C). At a constant host cell
density, we found a monotonic increase in the number of Bb-infected ECs
with increasing MOI. To confirm that Bb is internally localized within
ECs, we also performed differential labeling of extracellular Bb-GFP
cells (inside/outside labeling) followed by 3D confocal microscopy
([78]Figures 1D and [79]S1A–S1C). Samples were fixed shortly after
exposure and specifically labeled with anti-Bb antibody under
nonpermeabilizing conditions. This inside/outside labeling method
allowed us to distinguish between extracellular Bb (GFP-positive and
antibody-labeled Bb) and those that resided within ECs (only
GFP-positive). We observed both internally-localized spirochetes as
well as spirochetes adhering to the EC surface or that had
transmigrated beneath the EC monolayer by 4 hpe. Although the number of
bacteria adhering to host cells or being internally localized
significantly increased from 4 to 8 hpe, we found no significant
differences between 8 and 24 hpe ([80]Figures 1E and 1F). In addition,
the internalization efficiency remained constant irrespective of how
long ECs were exposed to Bb ([81]Figure 1G). These findings suggest
that bacterial adhesion and internalization significantly increase
during the first hpe. They also raise the question of whether the lack
of changes observed between 8 hpe versus 24 hpe could be because of Bb
persisting in a viable but not proliferative state onto or within ECs,
thus resulting in a constant adhesion rate and internalization
efficiency between 8 hpe and 24 hpe.
Figure 1.
[82]Figure 1
[83]Open in a new tab
ECs internalize Bb in a dose-dependent manner
(A) Darkfield (left) and fluorescence image (right) of Bb
constitutively expressing GFP (Bb-GFP) grown to a concentration of
5 × 10^7 bacteria/mL in BSK-H media.
(B and C) Barplots of percentage of ECs infected with Bb versus MOI
(mean ± SD) (B) and corresponding histograms of the logarithm of Bb-GFP
fluorescence intensity per cell for ECs infected with different MOIs of
Bb (N = 3 replicate wells shown in different colors) (C). The histogram
of control unexposed cells is shown in red. Based on the
autofluorescence of the control group, a gate is defined showing what
is considered non-exposed (left, GFP-) and exposed (right, GFP+).
(D) Images of fixed samples of ECs exposed to Bb-GFP at a MOI = 11 at 4
hpe. Inside/outside staining was used to confirm internal localization
of Bb. Left to right: brightfield image superimposed to maximum
intensity projection of EC nuclei (blue), Bb-GFP fluorescence, antibody
fluorescence of non-internalized adhering bacteria and overlay of the
last two channels. White circle indicates an internalized spirochete.
(E–G) Barplots of all Bb-GFP spirochetes per host cell nucleus (E),
inside Bb-GFP spirochetes per host cell nucleus (F), and invasion
efficiency (inside/outside Bb) (mean ± SD, WRST: ∗∗p<0.01, ns: non
signficant) at different time points after exposure. N = 20 fields of
view were segmented and analyzed. See also [84]Figure S1 and [85]Video
S1.
Video S1. Time-lapse movie showing single Bb spirochetes within a
microscope slide, related to Figure 1
Time-lapse recording of Bb spirochetes constitutively expressing GFP
(Bb1286, Bb-GFP) within a microscope slide. Phase contrast (left) and
Bb-GFP fluorescence (right) are shown. Bb-GFP were grown to a
concentration of 5x10^7 bacteria/mL in BSK-H medium and were imaged
before EC exposure to determine exact Bb concentration. Scale bar and
corresponding times are indicated.
[86]Download video file^ (7.1MB, mp4)
Host EC motility transiently reduces during early Bb exposure
To examine in more detail how do Bb-GFP interact spatiotemporally with
ECs in monolayer and what changes in EC kinematics those interactions
could possibly induce, we monitored ECs exposed to Bb-GFP overtime
using time-lapse epifluorescence microscopy ([87]Figure 2A and
[88]Video S2). EC nuclei were stained with Hoechst so that the motion
of ECs could be tracked 7 h before and up to 17 hpe to Bb-GFP by
applying particle image velocimetry onto successive images of the EC
nuclei ([89]Gui and Wereley, 2002). We found that EC displacements and
mean speed of motion decreased dramatically during the first few hours
after exposure but at later time points ECs recovered their speed to
levels just slightly lower than those before exposure
([90]Figures 2A–2C). In our recordings we also observed that at later
time points Bb formed aggregates. Motivated by these results, we used
the Bb fluorescence images to calculate the integral Bb fluorescence
across the whole field of view (FOV) to assess whether Bb continue to
grow at later time points. We found that the integral of Bb
fluorescence intensity increased progressively after bacteria had been
added on top of the host ECs and reached a peak at a time point that
depended on the MOI (the lower the MOI, the earlier this peak was
observed, [91]Figure S2A). Thereafter, Bb fluorescence decreased
overtime until it reached a plateau and remained constant until the end
of the recording (∼2 days after exposure). Irrespective of the MOI, no
changes in mean Bb fluorescence or total area occupied by Bb occurred
at 24 hpe and thereafter ([92]Figures S2A and S2B). To characterize
more precisely the changes in Bb morphology over the course of exposure
to ECs and to identify whether single spirochetes are still present at
late exposure, we used image segmentation followed by classification of
the Bb into three distinct classes namely: single Bb (class A, blue);
Bb networks (class B, magenta); and Bb aggregates (class C, green)
([93]Figure S2C). We found that shortly after exposure to Bb were
mostly in a single spirochete configuration, but overtime spirochetes
formed networks and eventually bacterial aggregates although we could
still observe motile single Bb spirochetes ([94]Figure S2D and
[95]Video S2). We then performed propidium iodide (PI) staining of ECs
exposed to Bb at 4 or 24 hpe to distinguish between live versus dead Bb
([96]Figure 2E) ([97]Krämer et al., 2016). Although we found an
increase in the total PI fluorescence and in the area in whichBb-GFP
co-localized with PI, at 24 hpe many of the bacterial aggregates
present were PI-negative suggesting they were still viable
([98]Figures S2E and S2F). To further corroborate this and rule out
that the recovery of EC motility at late times after exposure was
because of Bb death, we inspected supernatants of ECs exposed for 4 or
24 h to Bb-GFP at either MOI = 200 or MOI = 22 using higher resolution
epifluorescence microscopy ([99]Figures S3A and S3B). At 4 hpe at both
MOIs most of the spirochetes were motile and no morphological
aberrations could be observed either by dark-field or epifluorescence
microscopy. At 24 hpe, some spirochetes lacked motility at both MOI =
22 and MOI = 200 and some developed blebs, but single motile
spirochetes were still present. This finding was further confirmed when
we incubated bacterial supernatants from EC-Bb co-cultures at 4 or 24
hpe into BSK-H medium and observed acidification of the medium,
indicative of growth of the spirochetes under all conditions tested,
albeit to a lesser extent compared to Bb never placed into the EC
culture medium (data not shown). Thus, we can conclude that the
recovery of EC motility at later times after Bb exposure is not because
of bacterial death since viable spirochetes are still present, although
their morphology and proliferation rate differ from those they would
exhibit in BSK-H medium.
Figure 2.
[100]Figure 2
[101]Open in a new tab
ECs slow down during the early stages of Bb-exposure but their motility
is recovered at later time points
(A) Representative time-lapse epifluorescence microscopy images of ECs
in monolayer during exposure to Bb at a MOI =200. Columns show: phase
contrast image; Hoechst-stained EC nuclei; Bb-GFP fluorescence;
cellular displacements. Rows show different time points after exposure.
(B) Plot of mean host cell speed versus time (h) relative to the time
point when host cells were exposed to Bb (mean ± SD, N = 3 recordings).
Magenta dashed line corresponds to the time immediately after addition
of Bb.
(C) Boxplots of mean EC speed for ECs before Bb-exposure (tracked for 4
h) and after exposure to Bb at MOI = 200 up to 4 hpe. Different colors
correspond to different recordings and circles depict mean spread in
the whole field of view (mean ± SD, WRST: ∗p<0.05). See also
[102]Figure S2 and [103]Video S2.
Video S2. Time-lapse movie of HMEC-1 cells in monolayer exposed to high
(MOI=200) or low (MOI=22) dosage of Bb-GFP during the course of a 2-day
long recording, related to Figure 2
Time-lapse epifluorescence video-microscopy of HMEC-1 cells in
monolayer exposed to Bb-GFP at MOI = 200, followed by a second movie of
cells that were exposed to Bb-GFP at MOI = 22. Recording started at 7 h
before exposure and continued up to 33.2 hpe, with a time frame
interval of 10 min. Upper left panel shows the phase contrast image,
upper right panel shows the image of Hoechst-stained HMEC-1 nuclei
(blue), bottom left panel shows the Bb fluorescence image, and bottom
right panel the overlay of all channels. Scale bar and corresponding
times are indicated. Note that although aggregates are formed at late
exposure (∼24 hpe), viable motile spirochetes can still be observed at
both MOIs.
[104]Download video file^ (125.5MB, mp4)
Traction forces exerted by ECs onto their ECM are attenuated during early Bb
exposure
Adhesion of Bb onto ECs is mediated by the interaction between various
bacterial adhesins and ECM components including different integrin
subunits ([105]Coburn et al., 1998, [106]2021; [107]Ebady et al., 2016;
[108]Wu et al., 2011). We thus speculated that, during exposure, Bb
could potentially interfere with focal adhesion organization and
traction force generation of ECs in a stage-dependent manner through
its interactions with integrin subunits, which could explain the
attenuation we previously observed in EC motility. To test this
hypothesis, we used TFM to monitor ECs 4 h before exposure and up to
2 days after exposure to a high dosage of Bb (MOI = 200)
([109]Figure 3A, left). Although it might not accurately reflect a
“normal in vivo infection”, a high MOI was chosen for this experiment
to recognize a discernible effect in EC mechanotransduction and thereby
gain new mechanistic information as it is often done in such assays. At
the same time, we also recorded unexposed ECs to exclude the
possibility of changes in traction forces because of an increase in
cell confluence that can occur over the course of a day-long recording
([110]Figure 3A, right). We found that upon addition of Bb-GFP to ECs,
their strain energy (i.e., the mechanical work which ECs impact to
deform their ECM) increased abruptly 2-fold within the first 0.5 hpe
but decreased over time to 2-fold lower levels as compared to before
exposure or to unexposed cells ([111]Figure 3B). The decrease in
traction stresses and strain energy as compared to unexposed cells was
maintained up to approximately 15 hpe. After this point, the normalized
strain energy appeared identical in Bb-exposed and unexposed cells and,
in both cases, lower as compared to the beginning of the recording,
likely as a result of increased cell confluence ([112]Hur et al.,
2012). Surprisingly, when we correlated the ECM deformation maps, we
discovered that the turnover of traction adhesions (active EC adhesions
that transduce force to the ECM) of Bb-exposed cells was significantly
slower and less dynamic compared to that of unexposed cells
([113]Figure 3C), a feature that can also be observed by inspection of
the traction stress maps in [114]Figure 3A.
Figure 3.
[115]Figure 3
[116]Open in a new tab
ECs weaken their traction stresses during the early but not late stages
of Bb exposure
(A) Representative phase contrast image (first column), Bb-GFP
fluorescence (second column) and cellular traction stress map (third
column, Pa) for ECs in monolayer at different time points (rows) after
exposure to Bb-GFP (MOI =200). TFM was performed for ECs residing on
3 kPa ECM. The fourth and fifth columns show the corresponding phase
contrast image and cellular traction stress map for cells not exposed
to Bb.
(B) Normalized strain energy (mechanical work) imparted by ECs during a
TFM recording (mean ± SEM, N = 3 independent experiments). Strain
energy has been normalized with respect to the first value at the
beginning of each recording. Green: ECs exposed to Bb-GFP (MOI = 200);
black: unexposed ECs. Time (h) is represented relative with respect to
the time at whichBb was added. Time before exposure is shaded in red.
(C) Plot showing the cross-correlation coefficient versus time of the
cellular deformation maps obtained via TFM for successive frames
separated by different time delays for unexposed or Bb-exposed ECs
(MOI = 200) tracked for 24 hpe. An exponential decay function was
fitted into the data yielding a rate constant K= 0.01943 for unexposed
ECs and K= 0.01504 for Bb-exposed ECs (see [117]STAR Methods). See also
[118]Figure S3.
EC monolayer stresses are weakened only at early but not late Bb exposure
Our previous findings demonstrated that the traction stresses exerted
by ECs on their ECM are weakened during early but not late exposure to
Bb. However, cells in a monolayer are also able to transduce stresses
to each other through their intercellular junctions which are key in
regulating barrier function and tissue integrity ([119]Tambe et al.,
2011). Those stresses can be tensile or compressive, can result from
cells contracting, expanding, or being at rest in different spatial
locations and change overtime because of the reorganization of the
cytoskeleton and adhesions ([120]Figures 4 and [121]S4A). To measure
stresses everywhere within the EC monolayer and to examine how they
might change during Bb exposure, we used MSM to indirectly estimate
intra- and intercellular stresses within a cell monolayer (thereon
referred to as monolayer stresses).
Figure 4.
[122]Figure 4
[123]Open in a new tab
EC monolayer stresses lower during early but not late Bbexposure
(A) Sketch of the physical forces present in collectives of cells, and
analogue in humans, in 1D. Cells in the monolayer are subjected to
tensional and compressive stresses which at any instance balance with
the traction stresses on the substrate. Left sketches depict the whole
monolayer while right sketches focus on a single cell/human (green).
Top. Idealized situation in which all cells are pushed by adjacent
cells toward the center of the layer. Cells are subjected to
compressive stresses while there are no tensile stresses exerted
between cells (
[MATH: σI=0,σ
II :MATH]
). In the human analogue, humans are subjected to compressive stresses
that balance with each other and with the friction exerted in the
ground, resulting in a zero net force in each individual when there is
no movement. Bottom. Idealized situation in which all the cells in
the monolayer are pulled by adjacent cells away from the center of the
layer and are thus subjected to tensile stresses only (
[MATH:
σI,σ
II=0 :MATH]
). The analogue for humans is also shown.
(B) Representative phase contrast image with Bb-GFP fluorescence
superimposed (first column), monolayer tensile stresses (
[MATH: σI :MATH]
, second column) and compressive stresses (
[MATH:
σII
:MATH]
, third column) for ECs in monolayer at different time points (rows)
after exposure to Bb-GFP (MOI =200). Fourth-sixth columns show the
corresponding phase contrast image, monolayer tensile stresses and
absolute value of compressive stresses for ECs not exposed to Bb.
(C) Normalized mean monolayer tensile stresses (
[MATH: σI :MATH]
) as a function of time after exposure (mean ± SEM, N = 3 independent
experiments). Mean
[MATH: σI :MATH]
has been normalized with respect to the first value at the beginning of
each recording. Green: ECs exposed to Bb-GFP (MOI = 200); black:
unexposed ECs. Time (h) is represented relative to the time at whichBb
was added. Time before exposure is shaded in red. See also
[124]Figure S4.
ECs not exposed to Bb, showed supracellular fluctuations and high
heterogeneity in both monolayer tension
[MATH:
(σI)
:MATH]
and compression (
[MATH:
σII) :MATH]
, consistent with previous observations in epithelial cell monolayers
([125]Figure 4B, right) ([126]Bazellières et al., 2015). Interestingly,
the mean normalized monolayer tension (
[MATH: σI :MATH]
) in unexposed ECs remained nearly constant over the first 10 h of
imaging and progressively increased over the following 24 h,
potentially because of ECs becoming more confluent ([127]Figure 4C). On
the contrary, mean normalized monolayer tension
[MATH: (σI) :MATH]
exhibited a sharp increase over the first 30 min after Bb exposure but
remained markedly lower than that of unexposed cells thereafter and up
to approximately 20 hpe ([128]Figures 4B and 4C). After that time
point, no significant differences were observed between ECs exposed or
not to Bb ([129]Figure 4C). Collectively, our findings suggest that
both the EC traction stresses and monolayer stresses are significantly
weakened in cells exposed to Bb at early stages (<15 hpe) but they
display no differences at later points as compared to cells never
exposed to Bb.
EC integrins β1 and αvβ3 colocalize with Bb but their ventral organization or
that of F-actin is comparable to unexposed ECs
We next asked whether the changes in EC motility and traction force
generation at early times after exposure were because of alterations in
the organization of the cell cytoskeleton or of the focal adhesions. To
address that, we grew ECs in monolayer and exposed them (or not) to
Bb-GFP. Samples were fixed at 8 hpe, immunostained and inspected using
3D confocal microscopy. We first examined the F-actin cytoskeleton
using fluorescently labeled phalloidin and observed no major
differences when comparing non- or Bb-exposed cells ([130]Figures S5A
and S5B). Actin stress fibers were present in both cases, although
slightly more abundant in unexposed as compared to Bb-exposed cells,
however, the integral of phalloidin fluorescence intensity in a per
cell basis was similar in both conditions ([131]Figure S5C). Given the
known involvement of integrins αvβ3 and α5β1 in the attachment of Bb to
different host cell types ([132]Coburn et al., 1998; [133]Niddam
et al., 2017), in the organization of focal adhesions and EC migration
([134]Leavesley et al., 1993), we examined whether their localization
or abundance differed between non- or Bb-exposed ECs by imaging fixed
samples via 3D confocal microscopy ([135]Figures 5A–5D). Inspection of
the maximum projection images revealed that some but not all of the
spirochetes co-localized with both anti-β1 and anti-αvβ3 antibodies,
confirming that the observed signal did not originate from
bleed-through fluorescence ([136]Figures 5A and 5D). A more detailed
analysis would be needed to conclude whether this co-localization is
stochastic or not. The overall organization of those integrin subunits
at the basal surface of ECs, from where traction forces are transduced
to the extracellular matrix, did not differ dramatically as compared to
unexposed ECs, although the integral of anti-β1 antibody fluorescence
intensity per cell was increased in the Bb-exposed as compared to
unexposed ECs ([137]Figures S5D and S5E).
Figure 5.
[138]Figure 5
[139]Open in a new tab
EC integrins β1 and αvβ3 colocalize with Bb and β1 shows increased
localization compared to unexposed ECs
(A) Representative brightfield image of cells superimposed with the
Hoechst-stained nuclei image (first column), anti-β1 integrin antibody
fluorescence (second column, maximum intensity projection), Bb-GFP
fluorescence (third column, maximum intensity projection) and overlay
of the last two channels (fourth column) for ECs exposed to Bb-GFP for
8 h.
(B) Representative brightfield image of cells superimposed with the
Hoechst-stained nuclei image and anti-β1 integrin antibody fluorescence
for HMEC-1 not exposed to Bb.
(C) Boxplots of normalized mean anti-β1 antibody fluorescence intensity
per cell (mean ± SD, dots: individual cells) for ECs exposed to Bb-GFP
for 8 h or unexposed ECs. Normalization is done with respect to the
mean intensity of unexposed ECs. ∗∗: p<0.01, ns: not significant
(Wilcoxon rank-sum test).
(D–F) Same as in (A–C) but showing anti-αvβ3 integrin antibody
fluorescence. In panels (A) and (D) pink circles denote co-localization
of Bb-GFP and the indicated integrins while arrows point to Bb-GFP
cells that do not colocalize with integrins. See also [140]Figure S5.
RNA sequencing reveals distinct transcriptional profiles in Βb-exposed versus
unexposed ECs during early but not late exposure
To better understand which signaling processes might regulate the
changes in EC mechanotransduction in response to Bb-exposure and how
they might differ between early versus late time points after exposure,
we analyzed the EC transcriptome. To that end, we exposed ECs in
monolayer to Bb and extracted their RNA at 4, 24 and 48 hpe. In
parallel, at the same time points we also extracted the RNA from
unexposed cells (control cells) that were seeded at the same density.
We then performed RNA sequencing on these six different populations and
used four replicates per condition to determine differentially
expressed genes (DE-Gs) when comparing all six populations
([141]Table S1, see Sheets 1–6). As shown by the volcano plot in
[142]Figure 6A, at 4 hpe a significant number of genes was upregulated
in Bb-exposed ECs as compared to controls (78 genes) but only few were
downregulated (18 genes). On the contrary, at 24 hpe only 22 and 20
genes were significantly upregulated and downregulated, respectively,
in Bb-exposed ECs as compared to controls ([143]Figure 6B).
Interestingly and paradoxically, at 48 hpe 48 genes were significantly
upregulated and 67 genes were significantly downregulated in Bb-exposed
cells as compared to controls ([144]Figure 6C). However, when we
performed principal components analysis (PCA) on our samples (N = 4
replicates per condition), we observed two distinct clusters in the PCA
space only for the Bb-exposed versus unexposed samples at 4 hpe,
whereas the rest of the samples overlapped in the PCA space
([145]Figure 6D). Moreover, as expected, the unexposed ECs at 4 hpe
clustered closer to the rest of the samples in the PCA space whereas
the Bb-exposed ECs at 4 hpe were the only group that clustered on its
own as compared to the other samples.
Figure 6.
[146]Figure 6
[147]Open in a new tab
ECs upregulate innate immune signaling pathways at four but not 24 hpe
to Bb
(A–C) Volcano plots of differentially expressed genes (DE-Gs).
The-log[10] pvalues are plotted against the average log[2] fold changes
in expression. For each pair of compared conditions the upregulated
genes of each group are shown in the corresponding color. Each panel
refers to a different time after exposure as indicated.
(D) PCA of top genes that have ANOVA p value ≤0.05 on FPKM abundance
estimations. PC1 versus PC2.
(E) Pathway enrichment analysis. Bb-exposed ECs were compared to
unexposed ECs based on their enrichment score (-log[10]p). Resulting
barplots for the different times after exposure are shown only for
pathways that had-log[10]p>3. See also [148]Figure S6 and
[149]Table S1.
We then performed pathway enrichment analysis for the DE-Gs to
revealwhich pathways were significantly perturbed when comparing the
different groups ([150]Figure 6E and [151]Table S1, see Sheets 7–9).
Compared to unexposed ECs, Bb-exposed ECs showed significant
upregulation of 47 KEGG pathways and significant down regulation of
only one pathway at 4 hpe ([152]Table S1, see sheet 7). The pathways
with an enrichment score larger than three and with at least three
genes significantly upregulated in Bb-exposed ECs were in order of
decreasing enrichment score: (1) NFκΒ signaling pathway; (2) TNF
signaling pathway; (3) IL-17 signaling pathway; (4) Legionellosis; (5)
AGE-RAGE signaling pathway in diabetic complications; (6) NOD-like
receptor signaling pathway; (7) Kaposi’s sarcoma-associated herpesvirus
infection; (8) Rheumatoid arthritis (refer to [153]Table S1, sheet
seven for a list of the specific DE-Gs pertaining to each pathway).
Unlike Bb-exposed versus unexposed ECs at 4 hpe, the only KEGG pathway
that complied to the criteria listed above at 24 hpe was the NOD-like
receptor signaling pathway which contained only three genes
differentially regulated as opposed to seven at 4 hpe. Finally, at 48
hpe the only KEGG pathway that complied to the criteria listed above
was Mineral absorption, with the following five genes upregulated:
MT1F, MT1G, MT1M, MT1X (genes encoding metallothioneins) and SLC30A1
(gene encoding zinc transporter 1), all involved in copper and zinc
metabolism. Metallothioneins are cysteine-rich metal binding proteins
with high binding affinity for several metals, such as copper and zinc
([154]Calvo et al., 2017), both of which have previously been shown to
regulate biofilm formation ([155]Danilova et al., 2020). In addition,
metallothioneins are upregulated by host cells in response to biofilm
formation by other bacterial communities ([156]Smolentseva et al.,
2017).
Given the centrality of the NFκΒ and TNFα signaling pathways in many
types of infection and their recent involvement in modulating host cell
mechanics in response to infection with intracellular bacterial
pathogens ([157]Bastounis et al., 2021), we examined in more detailed
which specific genes of these two pathways were differentially
expressed during Bb-exposure ([158]Figure S6). At 4 hpe, we found that
genes encoding a number of cytokines were significantly upregulated in
Bb-exposed ECs, namely: CXCL2 (gene encoding the chemokine CXCL2),
CXCL1 (gene encoding the chemokine CXCL1), CXCL8 (gene encoding the
chemokine IL-8) and CCL2 (gene encoding the chemokine MCP1). Moreover,
upregulated were also the genes encoding the cell adhesion molecule
ICAM1 and EDN1, a vasoconstrictor peptide that is often released by ECs
in response to TNFα or NFκΒ activation ([159]Quehenberger et al., 2000;
[160]Bourque et al., 2011). Out of all those genes only CCL2 and CXCL1
remained significantly upregulated at later times after exposure (i.e.,
24 and 48 hpe) while the expression of the remaining genes returned to
basal levels, i.e., to those of unexposed ECs. Altogether the
upregulation of immunity pathways during the early but not late stages
of exposure correlates with the early changes in biomechanics we
observed in Bb-exposed ECs and is consistent with previous findings
([161]LaFrance et al., 2011).
Exposure of ECs to heat-inactivated Bb recapitulates only the early weakening
of EC mechanotransduction but not the reversal at late exposure
We speculated that the time-dependent changes in EC force transduction
during exposure to Bb could have been actively triggered by the
spirochetes. However, at later time points (24 hpe) we did observe a
decrease in Bb viability as assessed by PI staining. To rule out the
possibility that the observed changes in biomechanics were simply the
response of ECs to dead Bb, we exposed ECs to heat-inactivated Bb-GFP
at a high (MOI = 200) or low (MOI = 22) dosage and conducted TFM and
MSM 5 h prior and over 24 hpe. We discovered that irrespective of the
MOI, EC traction forces and strain energy weakened to a similar extent
in ECs exposed to heat-inactivated Bb and in those exposed to live Bb
([162]Figures 7A, 7B,[163]3A and 3B). Of interest, in response to
heat-inactivated Bb we observed neither the transient abrupt increase
in traction forces and strain energy nor their recovery at late
exposure (>15 hpe), which we observed upon exposure to live Bb.
Monolayer maximum tensile and compressive stresses of ECs were also
sustainably weakened upon exposure to heat-inactivated Bb-GFP at both
MOIs, but we did not observe the transient and abrupt increase that
occurred upon exposure to live Bb ([164]Figures 7A–7C,[165]4B and 4C).
Given that the weakening of EC force transduction upon early exposure
to live Bb was accompanied by an upregulation of innate immune
signaling pathways such as the NFκΒ pathway, we wondered whether we
were to observe such an effect also in response to heat-inactivated Bb.
To that end, we performed RT-PCR analysis on ECs exposed to
heat-inactivated Bb-GFP at 4 and 24 hpe, to test the expression of
three NF-κB target genes (namely, CXCL8, ICAM and NFKBIA) which we had
found upregulated at 4 but not 24 hpe to live Bb through RNA sequencing
analysis. Of interest, we discovered an upregulation of all those genes
at both 4 and 24 hpe and the extent of upregulation (approximately
2-fold) was similar to that observed just at 4 hpe for ECs exposed to
live Bb.
Figure 7.
[166]Figure 7
[167]Open in a new tab
ECs sustainably weaken their force transduction and upregulate NF-κB
target genes in response to heat-inactivated Bb
(A) Representative phase contrast image overlayed with heat-inactivated
Bb-GFP fluorescence (first column), EC traction stress map (second
column, Pa), monolayer tensile stresses
[MATH: (σI :MATH]
, third column, Pa) and absolute value of compressive stresses
[MATH: (σII
:MATH]
, fourth column, Pa) for ECs in monolayer at different time points
(rows) after exposure to heat-inactivated Bb-GFP (MOI =200). TFM was
performed for ECs residing on 3 kPa ECM.
(B) Normalized strain energy imparted by ECs during a TFM recording
(mean ± SEM, three independent experiments and N= 12 recordings in
total). Strain energy has been normalized with respect to the first
value at the beginning of each recording. Dark (light) green: ECs
exposed to heat-inactivated Bb with an MOI = 200 (MOI= 22); black:
unexposed cells. Time (h) is represented relative with respect to the
time at whichBb was added. Time before exposure is shaded in red.
(C) Same as panel B but showing the normalized mean EC monolayer
tensile stresses (
[MATH: σI :MATH]
) as a function of time after exposure to heat-inactivated Bb-GFP.
(D) Relative with respect to GAPDH expression levels of the indicated
NF-κB target genes obtained by RT-PCR. N = 3 independent experiments
were performed. Three conditions were tested namely ECs exposed to
nothing, to heat-inactivated Bb-GFP with an MOI = 200 for 4 h (blue) or
for 24h (red). From top to bottom normalized expression of the
following genes is shown: CXCL8, ICAM1, NFKBIA. Boxplots show the mean,
25^th and 75^th quartiles, different colors refer to replicates from
independent experiments, ∗: p<0.05, ∗∗: p<0.01, ∗∗∗: p<0.001, ∗∗∗∗:
p<0.0001 (Wilcoxon rank-sum test run for each condition’s distribution
with respect to control distribution). See also [168]Figure S7.
The intriguing correlation between weakening in EC mechanotransduction
and upregulation of NFκΒ target genes led us wonder whether EC force
transduction would change in response to a cytokine that is known to
upregulate NFκΒ like tumor necrosis factor alpha (TNFα) or to muramyl
dipeptide (MDP), which is found in the cell wall of many bacteria and
is known to upregulate NOD signaling and ΝFκΒ in certain cell types
([169]Stroka et al., 2012; [170]Urbano et al., 2017; [171]Lappas, 2013;
[172]Brandt et al., 2022). We discovered that addition of TNFα onto EC
monolayers led to an immediate and sustained increase of traction and
monolayer stresses while the biomechanical responses of MDP-exposed ECs
appeared identical to those of non-exposed ECs ([173]Figures S7A–S7C).
When we assessed the expression of the three NF-κB target genes (CXCL8,
ICAM and NFKBIA) via RT-PCR, we found a dramatic increase (10- to
100-fold compared to controls) in their expression both at 4 and 24 hpe
to TNFα ([174]Figure S7D) while there were no detectable changes in
expression of those genes for cells exposed to MDP (data not shown).
Thus, it appears that a dramatic upregulation of innate immune
signaling as obtained in response to TNFα leads to a fast and sustained
upregulation of EC force transduction. Whether TNFα is somehow related
to the strong immediate upregulation of force transduction upon
encounter of ECs with live Bb was not tested but could be the focus of
future studies.
Discussion
Cell-cell and cell-matrix adhesions of ECs are continuously remodeled
in response to biochemical and mechanical cues. This remodeling results
in dynamic alterations of the forces that cells transmit to their
matrix and to each other, which are regulated to a large extent through
the action of the actomyosin contractile apparatus ([175]Komarova
et al., 2017). Pro-inflammatory mediators and secreted cytokines can
reinforce ([176]Brandt et al., 2022) or alternatively release the
tensile stresses between ECs, thus enhancing vascular permeability
([177]Lee et al., 2011). A decrease in cell-ECM forces can also lead to
attenuation in EC monolayer stresses as a result of the crosstalk
between adherens junctions and focal adhesions ([178]Komarova et al.,
2017). Such attenuation in traction and monolayer stresses, similar to
what we observed during early exposure of ECs to Bb, could benefit the
bacteria by enhancing their transmigration through the leaky EC
monolayer. However, sustained, and prolonged impairment of cellular
physical forces could lead to extensive leakiness of the vessels, a
feature that could harm a pathogen like Bb in the first place by
rapidly killing the host and making a persistent infection impossible.
The weakening in EC force transduction during early Bb exposure which
we discovered, coincides with upregulation of a number of host cell
genes involved in innate immune signaling. Previous studies have
demonstrated an intricate link between innate immune signaling and
alterations in either intercellular stresses or permeability
([179]Bastounis et al., 2021; [180]Kisseleva et al., 2006; [181]Kempe
et al., 2005). We recently showed that NFκΒ activation in response to
infection with intracellular bacterial pathogens (L. monocytogenes and
a Rickettsia parkerimutant), leads to a decrease in traction forces of
epithelial cells in monolayer as compared to non-infected settings, and
a decrease in monolayer stresses as measured indirectly through laser
wounding ([182]Bastounis et al., 2021). In vivo, NFκΒ activation has
been explicitly linked to increased vascular EC permeability
([183]Kisseleva et al., 2006). It is possible that Bb spirochetes might
take advantage of the weakening in host cell mechanotransduction, which
correlates with upregulation of innate immune signaling at early
exposure, to facilitate their paracellular transmigration through ECs,
or even their transcellular transmigration through ECs (i.e., by taking
advantage of the EC intracellular tension weakening). Recently,
intravital imaging of the microvasculature of mice revealed that
infection with Bb triggers local recruitment of neutrophils which,
instead of killing the pathogens, leads to production of various
cytokines, particularly CCL2, thereby activating ECs and increasing
barrier permeability ([184]Tan et al., 2021). This in turn facilitates
transcellular but not paracellular transmigration of Bb to reach
distant tissues. Interestingly, we also found that ECs upregulated CCL2
expression throughout exposure to Bb, suggesting that some of the
spirochetes we observed intracellularly may just be en route of
transmigration through ECs. Production of additional cytokines, like
TNFα and IL-10, was increased in the study of Tan et al., most likely
because those cytokines are primarily produced by neutrophils. In vitro
assays similar to the one we developed here but additionally involving
co-culture with neutrophils could help shed more light into how ECs
biomechanics in response to Bb exposure is altered in the presence of
neutrophils. It should be noted that Bb might have additional ways of
altering host cell mechanics, e.g., by inducing changes in the
mechanical properties of the ECM to which host cells are anchored
directly or indirectly. For example, the Bb surface exposed protease
BbHtrA can directly degrade ECM proteins in various tissues including
the skin, joints and brain, which is expected to induce concomitant
changes in host cell mechanics ([185]Russell et al., 2013). In
addition, Bb infection leads to the upregulation of matrix
metalloproteases (MMPs, ECM degrading enzymes) by chondrocytes
([186]Behera et al., 2005), keratinocytes and macrophages ([187]Gebbia
et al., 2001). Although MMP upregulation, ECM stiffening and changes in
cell contractility are tightly correlated processes in the context of
cancer metastasis ([188]Haage and Schneider, 2014), their interplay
during Bb infection has yet to be uncovered.
The changes we found in EC kinematics, dynamics and gene expression in
response to Bb exposure are transient and progressively vanish as time
after exposure elapses. The absence of changes in EC gene expression at
late times after exposure to Bb is consistent with a previous
microarray analysis ([189]LaFrance et al., 2011). However, in this
study gene expression analysis of cells, including HMEC-1, at early Bb
exposure (1 or 3 hpe) revealed upregulation of pathways related to the
regulation of the actin cytoskeleton and focal adhesions which we do
not find to be upregulated through RNA sequencing of HMEC-1 at 4 hpe.
Rather, we found upregulation of a lot of pathways related to innate
immune signaling. A possible reason for this discrepancy could be that
in the LaFrance et al. study the noninfectious Bb strain HB19/KO1 was
used while we used the infectious Bb strain B31 5A4 NP1. Different Bb
strains present differences in genotype, clinical features and
inflammatory potential, therefore the changes they can induce in EC
gene expression and biomechanics could well be strain specific
([190]Cerar et al., 2016).
At late EC exposure to Bb, we observed the formation of Bb aggregates,
but single motile and viable spirochetes were still present in the
culture medium. Past studies have suggested that multiple factors can
lead to Bb aggregation ([191]Anderson and Brissette, 2021; [192]Sapi
et al., 2012; [193]Alban et al., 2000). However, in our assay we
determined that this is the result of the high MOI used, since it also
occurs when large amounts of spirochetes are cultured in their normal
growth medium (data not shown). To determine herein, whether the lack
of changes in EC gene expression and biomechanics at late exposure to
live Bb could be related to the status of Bb, and to understand whether
the time-dependent changes in EC force transduction represent and
active or passive response to Bb, we exposed ECs to heat-inactivated
Bb. We found that throughout the course of exposure EC
mechanotransduction was weakened and NF-κB target genes were
upregulated. It has been shown that Bb peptidoglycans (PG) and
lipoproteins such as the outer surface protein OspC are resistant to
heat and at least PG can induce NFκΒ activation in host cells
([194]Jutras et al., 2016, [195]2019; [196]Kumru et al., 2011) whereas
other studies have shown that exposure of different cell types to dead
Bb induce inflammatory mediators ([197]Parthasarathy et al., 2013;
[198]Wang et al., 2008). Whether any of those components are
responsible for the weakening in EC mechanotransduction could be tested
in future studies experimentally by addition of recombinant proteins or
PG components onto EC monolayers. However, when we added MDP onto ECs
we did not observe any change in EC force transduction but also any
upregulation of NFκΒ target genes in ECs (data not shown). On the
contrary, when we exposed ECs to TNFα, NFκΒ target genes were
dramatically upregulated (10- to 100-fold) and immediate and sustained
upregulation of EC force transduction was observed, consistent with
previous studies ([199]Stroka et al., 2012; [200]Urbano et al., 2017;
[201]Brandt et al., 2022). This result suggests that the relationship
between EC physical forces and innate immune signaling is likely not
monotonic. Finally, it is worth noting that the responses of ECs to
live versus heat-inactivated Bb exposed ECs particularly differed at
very early and late stages of exposure. First, upon exposure to
heat-inactivated Bb there was no abrupt increase in EC force
transduction, a feature that was observed upon exposure to live Bb.
This result suggests that the initial signal conveyed to ECs may arise
from adhesion of live Bb to ECs through some receptor that is possibly
denatured upon heat inactivation. The late reversal of EC gene
expression and biomechanics, a feature that was observed only in
response to live but not heat-inactivated Bb, suggests that this is not
a consequence of the loss of Bb viability because of prolonged exposure
to EC culture media. Rather, it could be because of changes in the Bb
state, including the metabolic or gene expression state. Whether this
response reflects what would happen in an actual in vivo infection is
unknown, but lack of host cell gene expression changes at one day after
infection with Bb has previously been shown in vitro ([202]LaFrance
et al., 2011) while changes in gene expression of Bb, which have been
shown to occur during actual in vivo infection, could well impact host
cell responses in a time-dependent manner ([203]Saputra et al., 2020).
To overcome a major limitation of in vivo approaches, namely, the fact
that multiple parameters can change in an unpredictable manner at once
limiting the strength of causal conclusions, herein we used an
elaborate in vitro assay that enabled us to measure the spatiotemporal
evolution of EC kinematics and dynamics during exposure to Bb in a
controllable manner while tweaking one parameter at a time. This in
turn allowed us to discover an intricate time-dependent link between EC
innate immune signaling and biomechanics during Bb exposure. Our assay
however is under static conditions and does not account for additional
extracellular physical forces that ECs experience in vivo. For example,
in the vasculature ECs are exposed to fluid shear stresses and
gradients which can greatly impact EC behavior (e.g., proliferation,
motility etc) and mechanotransduction ([204]Barakat and Lieu, 2003;
[205]Brandt et al., 2022; [206]Ostrowski et al., 2014; [207]Tan et al.,
2021). The fluid shear flow regime can also impact the way Bb adheres
on ECs ([208]Ebady et al., 2016). It is possible that apical fluid flow
might impact both innate immune signaling and physical EC forces and
thus the EC responses to Bb exposure might present distinct features as
compared to static cultures. Examination of the impact of apically
exposed fluid flow on EC biomechanics and the interactions with Bb can
be the focus of future studies. Moreover, the high MOI used in our
study might not reflect what would be observed in vivo, but we used it
to be able to measure discernible effects in the EC traction-monolayer
forces and signaling and thus gain new mechanistic information as it is
often done in such assays. Future studies could focus on uncovering
whether our conclusions are valid in in vivo systems.
Our discovery of the upregulation of innate immune signaling and
weakening of host EC force transduction upon early but not late
exposure to Bb underlines the importance of uncovering both the spatial
and temporal changes that occur during the course of exposure. Studying
how changes in host cell biomechanics and gene expression are
temporally and spatially modulated will reveal how pathogens like Bb
can subvert host physiology to their own benefit, that is, to
disseminate or render infection chronic. Such studies can also lend key
insights into how ECs regulate force transduction and barrier integrity
in health and during bacterial infections.
Limitations of the study
Herein we used human skin microvascular ECs, HMEC-1, as our model ECs
and pathogenic strains Bb1286 or GCB726, as our model Bb strains.
Examination of additional EC types and Bb strains would allow assessing
how generalizable or conversely EC type- or Bb strain-specific the
mechanisms we discovered are. Moreover, in this study we assessed only
the changes in the transcriptomics of host ECs. Performance of RNA
sequencing to investigate the changes in gene expression that Bb
undergoes during the course of exposure could reveal how changes in EC
gene expression are linked to alterations in bacterial transcriptomics.
Finally, our data suggest an active modulation of host EC mechanics and
biochemistry by Bb. Experiments with specific Bbmutant strains could
reveal specific Bb virulence factors that trigger the active responses
of ECs and could facilitate the identification of the specific
underlying mechanisms Bb employ to alter EC biomechanics. Finally,
investigation of the physical and “dual” transcriptomic responses of
host ECs and Bb in a more complex in vitro system which allows
co-culture of ECs with professional immune cells, would have enabled
examining the contribution of the host professional immune cells to
Bb-ECs interactions, and would have thus been more physiological.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
__________________________________________________________________
Rabbit polyclonal anti-Borrelia burgdorferi antibody Thermo Fisher
Scientific Cat# PA1-73004, RRID:[209]AB_1016668
Mouse monoclonal anti-integrin β1 Millipore Cat# MAB1987;
RRID:[210]AB_94493
Mouse monoclonal anti-integrin αvβ3 Sigma Cat# MAB1976; RRID:
[211]AB_2296419
__________________________________________________________________
Chemicals, peptides, and recombinant proteins
__________________________________________________________________
AlexaFluor546 phalloidin Thermo Fisher Scientific Cat# A22283
SulfoSanpah Thermo Fisher Scientific Cat# 22589
Collagen I rat tail Fisher Cat# A1048301
Acrylamide 40% solution Sigma Cat# A4058
Bisacrylamide solution 2% Fisher Cat# BP1404250
Aminopropyltriethoxysilane Sigma Cat# 919302
FluoSpheres™ Carboxylate-Modified Microspheres, 0.2 μm, yellow-green
fluorescent (580/605), 2% solids Invitrogen Cat# F8810
FluoSpheres™ Carboxylate-Modified Microspheres, 0.2 μm, yellow-green
fluorescent (660/680), 2% solids Invitrogen Cat# F8807
MCDB131 Gibco Cat# 10372019
Leibovitz’s L-15 medium, no phenol red Thermo Fisher Scientific Cat#
21083027
Fetal bovine serum Gemini Bio-Prod Cat# 900108
Hydrocortisone Sigma Cat# H0888
hEGF Sigma Cat# EG9644
L-Glutamine Fisher Cat# SH3003401
Hoechst 33342, trihydrochloride Invitrogen Cat# H3570
BSK-H Medium Sigma Cat# B3528
Rabbit serum Sigma Cat# R4505
Gentamicin Thermo Fisher Scientific Cat# 15710064
Propidium iodide Sigma Cat# P4170
Recombinant human TNF-α InvivoGen Cat# rcyc-htnfa
Muramyldipeptide - MDP | NOD2 ligand InvivoGen Cat# tlrl-mdp
__________________________________________________________________
Critical commercial assays
__________________________________________________________________
QIAshredder Kit Qiagen Cat# 79656
RNeasy Plus MicroKit Qiagen Cat# 74034
PrimeScript™ RT Reagent Kit Takara Bio Cat# RR037A
KAPA SYBR® FAST Kapa Biosystems Cat# KK4601
__________________________________________________________________
Deposited data
__________________________________________________________________
RNA-seq data This paper [212]GSE174545
__________________________________________________________________
Experimental models: Cell lines
__________________________________________________________________
HMEC-1 cells M. Welch ([213]Reed et al., 2014) Previously obtained from
Centers for Disease Control, Biological Products
Branch
__________________________________________________________________
Experimental models: Organisms/strains
__________________________________________________________________
Borrelia burgdorferi (strain Bb1286) J. Radolph ([214]Dunham-Ems
et al., 2009) N/A
Borrelia burgdorferi (strain GCB726) P. Kraiczy ([215]Moriarty et al.,
2008) N/A
__________________________________________________________________
Oligonucleotides
__________________________________________________________________
Primers for ICAM1 (forward: 5′ GAGCTTCGTGTCCTGTATGGC -3′, reverse: 5′
ACAGAGGTAGGTGCCCTCAA -3′) This paper N/A
Primers for CXCL8 (forward: 5′ CAGAGACAGCAGAGCACACA -3′, reverse: 5′
GGCAAAACTGCACCTTCACA -3′) This paper N/A
Primers for NFKBIA (forward: 5′ ATGTCAATGCTCAGGAGCCC -3′, reverse: 5′
CAGGTGAGCTGGTAGGGAGA -3′) This paper N/A
Primers for GAPDH (forward: 5′ GGGAAACTGTGGCGTGAT -3′, reverse: 5′
GAGTGGGTGTCGCTGTTGA -3′) This paper N/A
__________________________________________________________________
Software and algorithms
__________________________________________________________________
ImageJ [216]Schneider et al., 2012 [217]https://imagej.nih.gov/ij/
MicroManager Open Imaging [218]https://www.micro-manager.org/
MATLAB MathWorks
[219]http://www.mathworks.com/products/matlab/?requestedDomain=www.math
works.com
GraphPad Prism v6 GraphPad
[220]http://www.graphpad.com/scientific-software/prism/
Hisat 2 ([221]Kim et al., 2015)
[222]https://ccb.jhu.edu/software/hisat2/index.shtml)
Cutadapt ([223]Martin, 2011)
[224]https://cutadapt.readthedocs.io/en/stable/
R package GAGE ([225]Luo et al., 2009)
[226]https://bioconductor.org/packages/release/bioc/html/gage.html
R package “Pathview” ([227]Luo et al., 2017)
[228]https://www.bioconductor.org/packages/release/bioc/html/pathview.h
tml
ABAQUS Dassault systèmes
[229]https://www.3ds.com/products-services/simulia/products/abaqus/
QuantStudio™ Design and Analysis Software v2 Applied Biosystems
[230]https://www.thermofisher.com/de/en/home/global/forms/life-science/
quantstudio-3-5-software.html
Imaris Bitplane [231]https://imaris.oxinst.com/
[232]Open in a new tab
Resource availability
Lead contact
Further information and requests for reagents may be directed to and
will be fulfilled by the Lead Contact Effie Bastounis
[233]effie.bastounis@uni-tuebingen.de (E.E.B.).
Materials availability
Materials developed in this study are available on request to the
corresponding author.
Experimental model and subject details
Cell culture
Human dermal microvascular endothelial cells HMEC-1 (generous gift from
the Welch lab, University of California, Berkeley previously obtained
from Centers for Disease Control, Biological Products Branch) were
maintained in MCDB131 medium (Fisher Scientific; 10372-019)
supplemented with 10% fetal bovine serum (GemBio, 900108), 10 ng/mL
epidermal growth factor (Sigma, E9644), 1 μg/mL hydrocortisone (Sigma,
H0888), and 2 mM L-Glutamine (Sigma, 56-85-9) ([234]Reed et al., 2014).
Cells were passaged 1:6 at 90% confluence.
Bacterial strains used in this study
We used Borrelia burgdorferi (Bb) constitutively expressing GFP, strain
Bb1286, indicated as Bb-GFP throughout the main text. This strain was a
generous gift from Justin Radolf’s lab, University of Connecticut
Health Center ([235]Dunham-Ems et al., 2009). For the experiments
involving exposure of human cells to heat-inactivated Bb-GFP we used
strain GCB726. This strain, similar to Bb1286, was generated by
electroporation of the infectious Bb strain B31 5A4 NP1 with the
shuttle vector pTM61 that contains all plasmids except the circular
plasmid cp9. The construction of this strain has been previously
described ([236]Moriarty et al., 2008). Bb1286 strain was generated
also from B31 5A4 NP1 which was then electroporated with a suicide
vector to produce a cp26 plasmid containing the GFP expressing gene
([237]Caimano et al., 2015). Thus, the two strains we used are almost
identical and just differ in only one plasmid.
Method details
Bacterial growth conditions and exposure of ECs to Borrelia burgdorferi
Borrelia burgdorferi (Bb) strain Bb1286 or GCB726 were grown to mid log
phase (approximately 5x10^7 bacteria/mL) in BSK-H media (Sigma, B3528)
supplemented with 50 μg/mL gentamicin ([238]Dunham-Ems et al., 2009).
For cultivation of strain GCB726, BSK-H medium (Bio&Sell, Feucht,
Germany) supplemented with 5.4% rabbit serum (Sigma, R4505), 50 μg/mL
gentamicin and 100 μg/mL kanamycin was used. Briefly, 10 μL of frozen
glycerol stock was added into a tube containing 1.5 mL of BSK-H media
supplemented with appropriate antibiotics. The tube was placed at 37°C
and until the concentration of the bacteria reached 2-5x10^6
bacteria/mL. The bacterial solution was then diluted at 1x10^3
bacteria/mL and bacteria were grown in fresh BSK-H with antibiotics at
37°C until a density of 5x10^7 bacteria/mL growing as single
spirochetes was reached (see [239]Figure 1A). Bacterial concentrations
were determined using a hemocytometer and a dark field microscope
(generously provided by the Mougous lab, University of Washington).
The day prior toBb-GFP exposure, ECs were seeded at a density of
2 × 10^5 cells/well on glass bottom wells of 24-well plates coated with
0.25 mg/mL rat tail collagen I (Sigma-Aldrich, C3867) or on 3 kPa
polyacrylamide hydrogels also coated with 0.25 mg/mL rat tail collagen
I. For microscopy recordings, cell medium was removed and, instead of
MCDB131, Leibovitz’s L-15 medium was used supplemented with 10% fetal
bovine serum, 10 ng/mL epidermal growth factor, 1 μg/mL hydrocortisone,
and 2 mM L-glutamine. For experiments involving tracking of host cell
nuclei, 1 μg/mL Hoechst (Thermofisher, D1306) was added in each well to
stain the cells’ nuclei for 10minat 37°C. Cells were then washed once
in warm media to remove excess stain. Just prior to infection bacteria
were spun down, washed once in PBS and then resuspended in cell media
so that minimal volumes of bacteria (1-30 μL) were added in each well
to achieve the target multiplicity of infection (MOI, bacteria/host
cell).
For generation of heat-inactivated Bb-GFP, bacteria were incubated at
56°C for 30 min as previously reported ([240]Replogle et al., 2021).
Subsequently, heat-inactivated Bbwere stored at 4°C to avoid damage of
the Bb membrane or morphology that can occur at lower temperatures.
Flow cytometry of ECs exposed to Bb-GFP
4 h post-exposure (hpe), ECs exposed to Bb-GFP at different MOIs were
washed 4 times with sterile phosphate-buffered saline (PBS) to get rid
of extracellular bacteria ([241]Otte et al., 2021). Cells were then
detached from the glass coverslip where they resided, by removing cell
medium and adding 200 μL of 0.25% trypsin-EDTA in each well for 10 min
at 37°C. Trypsin-EDTA cell-containing solutions in each well were then
pipetted up and down 6 times to ensure single cell suspensions and
200 μL of complete medium were added to inactivate trypsin in each
well. Suspensions were transferred into 35-μm cell strainers, (Falcon,
352235) and spun through at 500 x g followed by fixation in 1%
paraformaldehyde for 10 min on ice. Samples were then washed once in
PBS and stored in PBS with 1% BSA on ice or at 4°C. Flow cytometry
analysis was performed on a BD FACS Canto RUO analyzer (University of
Washington Cell Analysis Facility). 10,000-20,000 cells were analyzed
per each replicate. To ensure analysis of single ECs, the bulk of the
distribution of cell counts was gated using the forward versus side
scatter plot and thus discarding debris or cell doublets or triplets
from the analysis. In addition, a second gating step was applied after
measuring the fluorescence of control, unexposed cells and gating the
population of exposed cells accordingly to exclude cell
autofluorescence.
Bb segmentation and morphology-dependent classification
To segment and classify bacteria depending on their morphology we used
using IMARIS software (Bitplane) on the time-lapse epifluorescence
images of bacteria. We opted for smoothing using surfaces detail
parameter equal to 0.4 μm and thresholding was performed after
background subtraction (local contrast) with the diameter of the
largest sphere which fits into the object equal to 0.6 μm. An Imaris
9.6 built-in classifier was then used to classify bacteria to three
distinct categories, namely: (1) single objects (spirochetes or
spot-like structures); (2) bacterial networks; and (3) bacterial
aggregates. For machine learning classification of surfaces Imaris 9.6
uses the existing statistics values and in addition computes more
values that provide additional intensity and shape information. To
provide additional shape descriptors the position and radius of the
biggest sphere that fits entirely within the surface were computed and
a number of features were derived from this. The original Surfaces
statistics together with the machine learning statistics provide a lot
of information about the shape of a surface and can be very useful for
a machine learning classifier for surfaces as previously implemented
elsewhere ([242]Ranzato et al., 2007; [243]Koenderink and van Doorn,
1987). For training, the user instigated 15 distinct objects for each
class. For each instance of time the number of bacteria that fell in
each category was extracted as well as average surface area (μm^2) and
mean object fluorescence intensity as illustrated in [244]Figures 2D
and [245]S2A and S2B.
Fabrication of polyacrylamide hydrogels
Polyacrylamide hydrogel fabrication was done as previously described
([246]Bastounis et al., 2018, [247]2021). Glass-bottom plates with 24
wells (MatTek, P24G-1.5-13-F) were incubated for 1 h with 500 μL of 1 M
NaOH, then rinsed with distilled water, and incubated with 500 μL of 2%
3-aminopropyltriethoxysilane (Sigma, 919-30-2) in 95% ethanol for
5 min. After rinsing with water, 500 μL of 0.5% glutaraldehyde were
added to each well for 30 min. Wells were then rinsed with water and
dried at 60°C. To prepare polyacrylamide hydrogels of 3 kPa, mixtures
containing 5% acrylamide (Sigma, A4058) and 0.1% bis-acrylamide
(Fisher, BP1404-250) were prepared ([248]Bastounis et al., 2018). Two
mixtures were prepared, the second of which contained 0.2 μm
fluorescent beads at 0.03% (Invitrogen, F8811) for traction force
microscopy (TFM) experiments. 0.06% ammonium persulfate and 0.43% TEMED
were then added to the first solution to initiate polymerization.
First, 3.6 μL of the first mixture without beads were added at the
center of each well, capped with 12-mm untreated circular glass
coverslips, and allowed to polymerize for 20 min. After coverslip
removal 2.4 μL of the mixture containing tracer beads were added and
sandwiched again with a 12-mm untreated circular glass coverslip and
allowed to polymerize for 20 min. Next, 50 mM HEPES at pH 7.5 was added
to the wells, and coverslips were removed. Hydrogels were UV-sterilized
for 1 h and then activated by adding 200 μL of 0.5% weight/volume
heterobifunctional cross-linker Sulfo-SANPAH (Fisher, 22589) in 1%
dimethyl sulfoxide (DMSO) and 50 mM HEPES, pH 7.5, on the upper surface
of the hydrogels and exposing them to UV light for 10 min. Hydrogels
were washed with 50 mM HEPES at pH 7.5 and were coated with 200 μL of
0.25 mg/mL rat tail collagen I (Fisher, A1048301) in 50 mM HEPES at pH
7.5 overnight at room temperature. Next morning, the collagen coated
surfaces were washed with HEPES and gels were stored in HEPES.
Traction force microscopy (TFM)
TFM was performed as previously described ([249]del Álamo et al., 2007;
[250]Lamason et al., 2016). Briefly, in TFM, cells actively pull on
their ECM depending on how well their focal adhesions are organized and
connected to the underlying cytoskeleton, and cellular force generation
can be inferred from displacement of fluorescent tracer particles
embedded in the deformable ECM ([251]Bastounis et al., 2014; [252]del
Álamo et al., 2007). Prior to seeding ECs (as described above)
hydrogels were equilibrated with MCDB131 medium for 30 min at 37°C. ECs
were then seeded to a concentration of 2 x 10^5 cells/well directly
onto the hydrogels 24 h prior to Bb-exposure. 1 h prior to initiation
of the TFM recording, MCDB131 medium was replaced with L-15 medium
supplemented appropriately. Multi-channel time-lapse sequences were
acquired to image the tracer beads’ fluorescence, the bacterial
fluorescence, and the phase contrast image of ECs. Images were acquired
using an inverted Nikon Eclipse Ti2 with an EMCCD camera (Andor
Technologies) using a 40X 0.60NA Plan Fluor air objective and the
MicroManager software package ([253]Edelstein et al., 2014). The
microscope was surrounded by a box type incubator (Haison) maintained
at 37°C. Images were acquired every 10 min for approximately 3-8 h
before Bb-GFP bacteria were added (or not for control wells) on the
wells at an MOI of ∼200Bb/cell. Subsequently, at each time interval we
measured the 2D deformation of the substrate at each point using an
image correlation technique similar to particle image velocimetry
([254]Gui and Wereley, 2002).
We calculated the local deformation vector by performing image
correlation between each image and a non-deformed reference image which
we acquired by adding 10% SDS at the end of each recording to detach
the cells from the hydrogels. We used interrogation windows of 32 x 16
pixels (window size x overlap). Calculations of the two-dimensional
traction stresses that cell monolayers exert on the hydrogel are
described elsewhere ([255]Lamason et al., 2016; [256]Bastounis et al.,
2014). We calculated the strain energy (
[MATH: Us :MATH]
) as the mechanical work imparted by cells to deform the hydrogel:
[MATH: Us=12∫st(z=h)⋅u(z=h)ds :MATH]
(Equation 1)
where u(z = h) is the measured displacement vector field on the free
surface of the hydrogel, t is the calculated traction stress vector
field,
[MATH:
∫sds
:MATH]
represents the surface integral, h is the height of the hydrogel and
[MATH: z :MATH]
the vertical coordinate. For each field of view analyzed originating
from different wells we normalized the strain energy value with that of
time = 0 min, to avoid discrepancies due to slight differences in cell
confluence among wells. To quantitate how dynamic the traction
adhesions of ECs is we preformed 2D cross-correlations between
successive cell deformation maps (obtained via TFM) separated by
different time delays using the corr2 function in MATLAB (MathWorks)
that returns the 2D correlation coefficient. The resulting data shown
in [257]Figure S3 were then fitted to an exponential decay function of
the form:
[MATH: Y=(Y0<
/mn>−Plat
eau)⋅exp(−K⋅X)+Plateau :MATH]
(Equation 2)
where Y[0] is the Y value at time equal 0, Plateau is the Y value at
infinite times and K is the rate constant.
Monolayer stress microscopy (MSM)
We used MSM to measure the tensile and compressive stresses that ECs in
monolayer experience. These stresses arise due to the cells
contracting, expanding, or remaining in a resting state at different
spatial locations within the monolayer and are due to reorganization of
their cytoskeleton and adhesion complexes. These cytoskeletal
rearrangements lead to pushing or pulling forces that affect adjacent
cells, resulting in cell movements and changes in cell size and shape.
Even if a given cell is also deformed due to its own remodelling and
active behaviour, this cell deformation is a consequence of the stress
distribution in the monolayer. This distribution is governed by the
internal forces per unit of area, defining the monolayer configuration.
Consequently, the stress distribution changes at each point
([258]Figures 4A and [259]S4). For measuring intercellular forces
indirectly via MSM, we use the previously computed via TFM traction
stresses that the cells in the monolayer exert on their substratum
under the assumption that the traction stresses in the vertical
direction are negligible, as considered previously ([260]Lamason
et al., 2016). We also assume that traction stresses in the
hydrogel-monolayer interface occur only in the plane of the monolayer
(i.e., there is no component of the traction stresses in the normal
direction of the interface) as done previously ([261]Trepat et al.,
2009). Traction forces exerted by the cells on the hydrogel need to
balance with the forces exerted by the cells to each other in the
monolayer (third Newton’s law). To that end, we consider idealised
perfect cell-substrate adhesions.
To determine the monolayer tensile and compressive stresses, we make
several simplifications. First, we consider that the thickness of the
monolayer is constant and uniform. Second, regarding material
properties of the monolayer, we assume perfect cell-cell adhesions (the
monolayer is assumed to be a continuum) and the properties of the
monolayer are considered everywhere the same. That is all cells and
adhesions in the monolayer have the same mechanical properties and the
cell monolayer is considered as a linear elastic isotropic material
([262]Tambe et al., 2011). Third, we work under the small strain
assumption, so both the strains induced in the hydrogel and the
configuration changes in the monolayer at each time increment (time
distance between subsequent frames) are small. These assumptions
dramatically simplify the formulation of the problem, since we can work
in a two-dimensional framework assuming a plane stress formulation to
solve the problem. Thus, the stress distribution is assumed constant
through the monolayer thickness.
In this case, the equilibrium equations, compatibility, and
constitutive equations yield the Beltrami differential formulation:
[MATH:
∂2(σx+σy
)∂x2+∂2(σx+σy
)∂y2=−(1+ν)
h(∂t<
/mi>x∂x+∂ty
∂y
) :MATH]
(Equation 3)
The advantage of using the Beltrami equation is that we can compute
stresses at each point without knowing the elastic modulus of the
monolayer. This equation, together with the Newton’s equilibrium
equations, formulated at each point of the monolayer in a differential
way, yield the distribution of the stresses in the monolayer:
[MATH: ∂σx∂x+∂τyx∂y+tx
h=0∂τ<
mi>xy∂x+∂σy
∂y+ty
h=0 :MATH]
(Equation 4)
where v is the Poisson’s coefficient, t[x] and t[y]are the traction
forces in the monolayer in the
[MATH: x :MATH]
and
[MATH: y :MATH]
directions, respectively (components of traction stress vector field
t); σ[x], σ[y] are monolayer normal tension/compression stresses in the
x and y directions, τ[yx] and τ[xy] are the monolayer shear stresses in
x and y directions respectively. These shear stress components are
equal (τ[yx]=τ[xy]) due to equilibrium. All these stresses will be
different at each point of the monolayer ([263]Figure S4A).
Although stresses in the
[MATH: x−y :MATH]
directions are easy to retrieve, the physical meaning is difficult to
interpret since the
[MATH: x−y :MATH]
coordinate system is an arbitrary system, normally aligned with the
image and not with the behaviour of the cell. To overcome this
challenge, we transform our results into a new coordinate system
(I, II), principal coordinate system, in which there is no shear
component of stress ([264]Figure S4B). By aligning with the axis of
this coordinate system, we can compute the maximum monolayer tension
(σ[I]) and compressions (σ[II]) in the plane of the monolayer,
(σ[I]>σ[II]). In fact, the maximum tension occurs in a perpendicular
direction to the maximum compression. Both values of maximum tension
and compression and their directions vary along the different points of
the monolayer.
We solve the equations of the problem through a finite element
formulation ([265]Oñate, 2013) and implement the final equations into a
custom-made finite element code in MATLAB (R2020b). In the simulations,
the monolayer is discretized with square elements of the same size as
the pixels (3.648 × 3.648 μm^2) resulting in 4225 nodes and 4096
elements. As boundary conditions, the displacements along the edge of
the monolayer/image were set to zero. We choose arbitrary elastic
modulus for the monolayer, since in this formulation the stresses are
independent of this mechanical property of the monolayer. The Poisson’s
coefficient was set to 0.48. The source code is provided at
[266]https://github.com/ebastoun/Monolayer-Stress-Microscopy.
Characterization of host cell kinematics and bacterial aggregation
ECs were seeded to a concentration of 2 x 10^5 cells/well directly onto
the glass coverslips coated with 0.25 mg/mL collagen I rat tail, 24 h
prior to Bb-exposure. 1 h prior to initiation of time-lapse imaging,
cells were washed with PBS and incubated with 1 μg/mL Hoechst
(Thermofisher, D1306) to stain the ECs’ nuclei for 10 min at 37°C.
Wells were then washed with L-15 medium supplemented appropriately and
1 mL of this medium was added to each of the wells. Multi-channel
time-lapse sequences were conducted to acquire at each instance of time
the Hoechst-stained EC nuclear fluorescence, the bacterial fluorescence
and the phase contrast image of ECs, using an inverted Nikon Eclipse
Ti2 with an EMCCD camera (Andor Technologies) using a 40X 0.60NA Plan
Fluor air objective and the MicroManager software package
([267]Edelstein et al., 2014). The microscope was surrounded by a box
type incubator (Haison) maintained at 37°C. Images were acquired every
10 min for approximately 7 h before Bb were added on the wells at an
MOI of ∼200.
Subsequently, at each time interval we measured the 2D displacements
based on the image of the host cell nuclei using an image correlation
technique similar to particle image velocimetry ([268]Gui and Wereley,
2002). We calculated the local displacement vectors by performing image
correlation between subsequent images of the nuclei. We used
interrogation windows of 48 x 24 pixels (window size x overlap). We
calculated the mean displacement of cells before and after Bb-exposure
by finding the average magnitude when considering all displacements in
a particular field of view.
To quantify total bacterial fluorescence overtime, we used custom-built
MATLAB code to integrate the Bb-GFP fluorescence intensity overtime. To
characterize the aggregation of bacteria observed overtime we performed
segmentation using IMARIS software (Bitplane) to identify all the
objects (spirochetes or aggregates) at each instance of time. All
identified objects and their associated attributes (i.e., area, mean Bb
fluorescence intensity, ellipticity) were imported into MATLAB
(MathWorks) for further analysis, e.g., for creating scatter plots of
object ellipticity versus object area or object mean fluorescence
versus object area.
RNA isolation and RNA sequencing
For sample preparation HMEC-1 cells were cultured in MCDB 131 medium
(Fisher Scientific, 10372-019) supplemented with 10% fetal bovine serum
(GemBio; 900-108), 10 ng/mL epidermal growth factor (Sigma; E9644),
1 μg/mL hydrocortisone (Sigma; H0888), and 2 mM L-glutamine (Sigma;
56-85-9). To generate confluent cell monolayers, 24-well plates
glass-bottom for microscopy were coated with 50 μg/mL rat-tail
collagen-I (diluted in 0.2 N acetic acid) for 1hat 37°C, air-dried for
15 min, and UV-sterilized for 30 min in a biosafety cabinet. ECs were
seeded at a density of 2 × 10^5 cells/well. 24 h post-seeding ECs were
exposed to Bb (MOI = 200) or not. At different times post-Bb-exposure,
namely 4 h, 24 h or 48 hpe, four replicates per condition were lysed
using the QIAshredder Kit by adding 200 μL of lysis buffer in each well
(Qiagen, 79656). mRNA was harvested using the RNeasy Plus MicroKit
(Qiagen, 74004) and eluted in 30 μL RNAase free water. A NanoDrop
ND-1000 spectrophotometer was used to determine concentration (abs 260)
and purity (abs260/abs230) of total RNA samples. Total RNA from each
sample was 1-2 μg. Agarose gel electrophoresis was used to check the
integrity of the RNA in all the samples (performed by Arraystar Inc.).
For sequencing library preparation, the subsequent steps were followed:
1. Total RNA was enriched by oligo (dT) magnetic beads (rRNA removed);
2. RNA-seq library preparation using KAPA Stranded RNA-Seq Library Prep
Kit (Illumina), which incorporates dUTP into the second cDNA strand and
renders the RNA-seq library strand-specific. The completed libraries
were qualified with Agilent 2100 Bioanalyzer and quantified by absolute
quantification qPCR method. To sequence the libraries on the Illumina
NovaSeq 6000 instrument, the barcoded libraries were mixed, denatured
to single stranded DNA in NaOH, captured on Illumina flow cell,
amplified in situ, and subsequently sequenced for 150 cycles for both
ends on Illumina NovaSeq 6000 instrument. Raw sequencing data that
passed the Illumina chastity filter were used for further analysis.
Trimmed reads (trimmed 5′, 3′-adaptor bases) were aligned to the
reference genome (GRCh37). Based on alignment statistical analysis
(mapping ratio, rRNA/mtRNA content, fragment sequence bias), we
determined whether the results could be used for subsequent data
analysis. To examine the sequencing quality, the quality score plot of
each sample was plotted. The quality score Q is logarithmically related
to the base calling error probability (P): Q = −10log[10](P). For
example, Q30 means the incorrect base calling probability to be 0.001
or 99.9% base calling accuracy. After quality control, the fragments
were 5′, 3′-adaptor trimmed and filtered ≤20 bp reads with Cutadapt
software ([269]Martin, 2011). The trimmed reads were aligned to the
reference genome with Hisat 2 software ([270]Kim et al., 2015). In a
typical experiment, it is possible to align 40 ∼ 90% of the fragments
to the reference genome. However, this percentage depends on multiple
factors, including sample quality, library quality and sequencing
quality. Sequencing reads were classified into the following classes:
(1) Mapped: reads aligned to the reference genome (including mRNA,
pre-mRNA, poly-A tailed lncRNA and pri-miRNA); (2) mtRNA and rRNA:
fragments aligned to rRNA, mtRNA; and (3) Unmapped: reads that are not
aligned.
Differentially expressed genes and differentially expressed transcripts
were calculated. The novel genes and transcripts were also predicted.
The expression level (fragments per kilobase of transcript per million
mapped reads (FPKM) value) of known genes and transcripts were
calculated using R package ballgown by estimating the transcript
abundances with StringTie. The number of identified genes and
transcripts per group was calculated based on the mean of FPKM in group
≥0.5. The FPKM value was calculated with the formula:
[MATH: FPMK=C×106L×N :MATH]
, where C is the number of fragments that map to a certain
gene/transcript, L is the length of the gene/transcript in Kb and N is
the fragments number that maps to all genes/transcripts. Differentially
expressed gene and transcript analyses were performed with R package
ballgown. Fold change (cutoff 1.5), p-value (≤0.05) and FPKM (≥0.5 mean
in one group) were used for filtering differentially expressed genes
and transcripts.
Principal component analysis (PCA) and mRNA function enrichment analysis
Principal Component Analysis (PCA) and Hierarchical Clustering scatter
plots and volcano plots were calculated for the differentially
expressed genes in R or Python environment for statistical computing
and graphics. PCA was performed using the plotPCA function in R with
genes that had the ANOVA p value ≤0.05 on FPKM abundance estimations
(Not available for samples with no replicates). Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathway analyses of the whole data set of DEG
were performed using the R package GAGE “Generally Acceptable Gene set
Enrichment” (GAGE v.2.22.0) package implemented in R ([271]Luo et al.,
2009). The analysis allowed us to determine whether the differentially
expressed mRNAs were enriched in certain biological pathways. The
p-values calculated by Fisher’s exact test are used to estimate the
statistical significance of the enrichment of the pathways between the
two groups. The R package “Pathview” v.1.12.0 and KEGGraph v1.30.0 were
used to visualize gene set expression data in the context of functional
pathways ([272]Luo et al., 2017).
Inside/outside bacterial labeling, immunofluorescence microscopy and image
processing
Unexposed or Bb-exposed ECs in monolayer on glass coverslips coated
with 0.25 mg/mL collagen I were washed with PBS and incubated with
1 μg/mL Hoechst (Thermofisher, D1306) to stain the cells’ nuclei for
10 min at 37°C. Wells were then washed twice in PBS and then fixed with
4% EM grade formaldehyde in PBS for 10 min. Samples were washed again
with PBS and stored until further processing.
For staining only extracellular Bb cells inside/outside labeling was
performed: samples were blocked for 30 min with 5% BSA in PBS and then
incubated with primary rabbit polyclonal anti-Bb antibody (Thermo
Fisher Scientific, PA1-73004) diluted 1:100 in PBS containing 2% BSA
for 1 h, at room temperature. Samples were washed in PBS three times
and then incubated with Alexa Fluor 546 goat anti-rabbit secondary
antibody (Invitrogen, A-11035) diluted 1:250 in PBS containing 2% BSA
for 1 h. Samples were washed three times in PBS and stored in 1 mL PBS
for imaging. N>1000 cells were analyzed per condition. For imaging, we
used an inverted Nikon Diaphot 200 with a charge-coupled device (CCD)
camera (Andor Technologies) and a 40× air Plan Fluor NA 0.60 objective.
The microscope was controlled by the MicroManager software package. For
differential immunostaining, all “green” bacteria associated with
individual cells were counted as adherent (because at the time of
fixation they were adhering on the host cell surface or because prior
to internalization they were adherent); bacteria that were both “green”
and “red” (due to antibody binding) were counted as non-internalized.
Nuclei number was identified by running a custom-made script in MATLAB
(Mathworks).
For immunostaining of cytoskeletal elements, fixed cells were first
permeabilized for 5 min in 0.2% Triton X-100 in PBS and washed again
with PBS. Samples were then blocked for 30 min with 5% BSA in PBS and
then incubated with primary antibodies (mouse monoclonal anti-integrin
β1, Millipore, MAB1987; mouse monoclonal anti-integrin αvβ3, Sigma,
MAB1976) diluted 1:100 in PBS containing 2% BSA for 1 h. Samples were
washed in PBS three times and then incubated with the appropriate
secondary fluorescent antibodies (Invitrogen) diluted 1:250 in PBS
containing 2% BSA for 1 h. For actin staining we used 0.2 μM
AlexaFluor546 phalloidin (Thermo Fisher Scientific, A22283). Samples
were washed three times in PBS and stored in 1 mL PBS for imaging. For
confocal imaging we used a Yokogawa W1 Spinning Disk Confocal with
Borealis upgrade on a Leica DMi6 inverted microscope with a 50um Disk
pattern, a 60x 1.4NA Plan Apo oil objective and MicroManager software.
Z-stacks were taken with an interval of 0.2 μm.
For quantification of the relative abundance of F-actin or integrins β1
and αvβ3 on Bb-exposed versus not EC monolayers on a per cell basis the
pipeline described was followed. Custom made codes were written in
MATLAB (Mathowrks). Briefly, the images of the Hoechst-stained nuclei
were used to segment the host cell nuclei and identify their centroids.
The MATLAB function voronoi2mask was used to perform centroidal voronoi
tessellation and thus approximate the boundaries of each cell in the
monolayer. For the area occupied by each cell the integral of
fluorescence intensity of each cytoskeletal component was then
calculated and depicted as a single dot on the boxplots shown in
[273]Figures 5C, 5F and [274]S5B.
Propidium iodide (PI) staining, imaging and quantitation of co-localization
of Bb with PI
PI (Millipore Sigma, P4170-10MG) at a concentration of 5 μM was added
in ECs in monolayer exposed to Bb (MOI = 200) at the time of exposure,
as described previously to assess dynamically bacterial death
([275]Krämer et al., 2016). ECs were residing in wells on 24-well
plates were imaged for 24hat 37 °C using the IncuCyte S3 time lapse
microscopy system (Sartorius) equipped with an IncuCyte S3 20 x PLAN
FLUOR objective. Phase contrast images, images of bacterial
fluorescence and of PI fluorescence were acquired every 90 min. For
quantification of co-localization between Bb-GFP and PI stain the
pipeline described below was followed. Images of bacterial fluorescence
were segmented so that bacterial objects were identified. Next, images
of PI fluorescence were also segmented. Then the percentage of pixels
occupied by bacteria that are also PI positive was determined.
Co-localization index was defined as the ratio of pixels that are both
occupied by bacteria and are PI-positive to the total number of pixels
occupied by bacteria. The source code is provided at
[276]https://github.com/ebastoun/borrelia_PI_colocalisation.
RT-PCR
To assess expression of NFκΒ target genes, ECs were seeded until
monolayers were formed (∼4x10^5 cells/well of 24 well plate) as
described above. At different time points, namely 4 h or 24 h
post-treatment with TNFα or MDP, or post heat-killed Bb exposure, two
to three replicates per condition per experiment were lysed using the
QIAshredder Kit by adding 350 μL of lysis buffer in each well (Qiagen,
79656). mRNA was harvested using the RNeasy Plus MicroKit (Qiagen,
74034) and eluted in 30μL RNAase free water. RNA concentrations were
measured spectrophotometrically (NanoDrop) and were comparable between
conditions. cDNA was prepared using the PrimeScript™ RT Reagent Kit
(Takara Bio, RR037A). RT-PCR was performed using the KAPA SYBR® FAST
mix (Kapa Biosystems, KK4601) and the QuantStudio 3 Real-Time PCR
system (Applied Biosystems). Genes of interest were amplified using the
appropriate primers for ICAM1 (forward: 5′ GAGCTTCGTGTCCTGTATGGC -3′,
reverse: 5′ ACAGAGGTAGGTGCCCTCAA-3′); for CXCL8 (forward: 5′
CAGAGACAGCAGAGCACACA -3′, reverse: 5′ GGCAAAACTGCACCTTCACA -3′); for
NFKBIA (forward: 5′ ATGTCAATGCTCAGGAGCCC -3′, reverse: 5′
CAGGTGAGCTGGTAGGGAGA -3′); for GAPDH (forward:5′ GGGAAACTGTGGCGTGAT
-3′, reverse: 5′ GAGTGGGTGTCGCTGTTGA -3′). Analysis was performed using
the QuantStudio™ Design and Analysis Software v2 and the Comparative CT
Method (2^−ΔΔCt). GAPDH was used as the reference gene.
Quantification and statistical analysis
Statistical parameters and significance are reported in the Figures and
the Figure Legends. Data are determined to be statistically significant
when p < 0.05 or p <0.01 by an unpaired Student’s T-Test (USTT), or
Wilcoxon Rank Sum Test (WRST), where indicated. As such, asterisk
denotes statistical significance as compared to indicated controls. For
inside/outside labeling 20 fields of view (FOV) were analyzed
consisting of an average 158 ± 60 host cell nuclei per FOV. For
statistical analysis of EC kinematics a large number of cells pertained
in each field of view (FOV) and average of cells’ displacements were
calculated in each FOV and overtime for three independent experiments.
The same applies for the traction force microscopy recordings. In the
boxplots and barplots midlines denote mean value and whiskers the
standard deviation (vertical bars). Statistical analysis was performed
in GraphPad PRISM 8.
Acknowledgments