Article

https://doi.org/10.1038/s41467-023-43885-w

Targeted treatment of injured nestmates
with antimicrobial compounds in an ant
society
Received: 26 May 2023

Check for updates

1234567890():,;

1234567890():,;

Accepted: 20 November 2023

Erik. T. Frank 1,2 , Lucie Kesner 3, Joanito Liberti 1,3, Quentin Helleu 1,4,
Adria C. LeBoeuf 5, Andrei Dascalu1, Douglas B. Sponsler2, Fumika Azuma6,
Evan P. Economo6,7, Patrice Waridel 8, Philipp Engel 3, Thomas Schmitt 2 &
Laurent Keller1

Infected wounds pose a major mortality risk in animals. Injuries are common
in the ant Megaponera analis, which raids pugnacious prey. Here we show
that M. analis can determine when wounds are infected and treat them
accordingly. By applying a variety of antimicrobial compounds and proteins
secreted from the metapleural gland to infected wounds, workers reduce the
mortality of infected individuals by 90%. Chemical analyses showed that
wound infection is associated with speciﬁc changes in the cuticular hydrocarbon proﬁle, thereby likely allowing nestmates to diagnose the infection
state of injured individuals and apply the appropriate antimicrobial treatment. This study demonstrates that M. analis ant societies use antimicrobial
compounds produced in the metapleural glands to treat infected wounds
and reduce nestmate mortality.

Infections are a major mortality risk in animals1,2, with the risk of
transmission of contagious pathogens being particularly lifethreatening in group living animals3. This has led to a suite of
pathogen-induced changes in social interactions, like social distancing,
sickness cues, and medical care4–6. In injured individuals, the major
barrier against infections (the cuticle or epidermis) is damaged and
therefore provides an easy entry point for life-threatening infections7.
Recently, several mammals have been shown to lick wounds to apply
antiseptic saliva1,5. However, the efﬁcacy of these behaviors remains
largely unknown and occur irrespective of the state of the wound.
In social insects, interactions to combat pathogens range from
preventive measures like nest disinfection or allogrooming to moribund individuals leaving the nest to die in isolation or the destructive
disinfection of their infected brood3,8–10. But if and how social insect

colonies care for injured individuals that were exposed to pathogens
remains poorly understood. Workers of the predatory ant Megaponera
analis have been shown to care for the injuries of nestmates2,7, which
are common because this ant feeds exclusively on pugnacious termite
species11. As many as 22% of the foragers engaging in raids attacking
termites have one or two missing legs2. Injured workers are carried
back to the nest where other workers treat their wounds, by licking and
grooming the wound during the ﬁrst three hours after injury7. When
the wounds of injured workers are not treated by nestmates, 90% of
the injured workers die within 24 h after injury7, but the mechanisms
behind these treatments are unknown. The aim of this study is therefore to identify the cause of death in injured individuals and the
potential mechanisms involved in the detection and treatment of
injuries.

1

Department of Ecology and Evolution, Biophore, University of Lausanne, CH-1015 Lausanne, Switzerland. 2Department of Animal Ecology and Tropical
Biology, Biocenter, University of Würzburg, D-97074 Würzburg, Germany. 3Department of Fundamental Microbiology, Biophore, University of Lausanne,
CH-1015 Lausanne, Switzerland. 4Structure et Instabilité des Génomes, Muséum National d’Histoire Naturelle, CNRS UMR 7196, INSERM U1154, 43 rue
Cuvier, F-75005 Paris, France. 5Department of Biology, University of Fribourg, CH-1700 Fribourg, Switzerland. 6Biodiversity and Biocomplexity Unit,
Okinawa Institute of Science and Technology Graduate University, Onna 904-0495, Japan. 7Radcliffe Institute for Advanced Study, Harvard University,
Cambridge 02138, USA. 8Protein Analysis Facility, Génopode, University of Lausanne, CH-1015 Lausanne, Switzerland.
e-mail: erik.frank@uni-wuerzburg.de; laurentkeller01@gmail.com

Nature Communications | (2023)14:8446

1

Article
Here we show that the gram-negative bacterium Pseudomonas
aeruginosa can cause lethal infections in injured M. analis workers. We
found that infected wounds are treated more often than sterile
wounds and that this correlates with changes in the cuticular hydrocarbon proﬁle of infected individuals. We describe the use of the
metapleural gland to treat infected wounds and quantify its content,
identifying 112 chemical compounds and 41 proteins in the gland’s
secretion, half of which have antimicrobial or wound healing properties. Overall, this study demonstrates that the targeted use of antimicrobials to treat infected wounds is effective in decreasing mortality
in injured individuals.

Results and discussion

https://doi.org/10.1038/s41467-023-43885-w

P. aeruginosa (OD = 0.05). While the mortality of infected ants kept in
isolation was 93%, mortality of infected ants that had been returned
to their nestmates was only 8% (least square means: DF = 142;
Z = −2.94; P = 0.01; Fig. 2a, Supplementary Fig. 2c, and Supplementary Table 4). There were major differences in the increase in Pseudomonas load after injury between ants kept with or without their
nestmates (Fig. 2b & Supplementary Table 5). The bacterial load of
infected ants kept with their nestmates did not increase signiﬁcantly
from 2 and 11 h after injury (least square means: DF = 18; t = −0.037,
P = 1; Fig. 2b). By contrast, there was a 100-fold increase in bacterial
load for infected ants kept in isolation (least square means: DF = 18;
t = −4.832, P < 0.001; Fig. 2b).

Pathogen identiﬁcation and mortality cause

Wound care behavior

To investigate whether the high mortality of injured individuals is
due to infection by pathogens, we collected soil from the natural
environment and applied it to the wounds of experimentally injured
workers (i.e., a sterile cut in the middle of the femur on the hind leg
of an otherwise healthy ant). After 2 h, injured ants exposed to the
soil (hereafter referred to as “infected ants”) had ten times higher
bacterial loads in the thorax than similarly injured individuals
exposed to sterile phosphate-buffered saline (PBS, hereafter referred
to as “sterile ants”; least square means: degrees of freedom (DF) = 35;
t = −3.08; P = 0.01; Fig. 1a and Supplementary Table 1). After 11 h,
bacterial load further increased in infected ants (least square means:
DF = 35; t = −3.66; P = 0.003), while there was no such increase in
sterile ants (least square means: DF = 35; t = −1.54; P = 0.26; Fig. 1a). As
a result, after 11 h the bacterial load was 100 times higher in infected
than sterile ants (least square means: DF = 35; t = −5.21; P < 0.001;
Fig. 1a). A microbiome analysis further revealed major differences in
bacterial species composition and abundance between the two
groups of ants (ADONIS: F(1,39) = 17.45; R2 = 0.31; P < 0.001; Fig. 1b and
Supplementary Fig. 1a, b), with three potentially pathogenic bacterial
genera increasing in absolute abundance in the thorax of infected
ants at both time-points: Klebsiella, Pseudomonas and Burkholderia
(Fig. 1b and Supplementary Table 2).
To investigate if the differences in bacterial abundance and
composition affect survival, we either placed infected and sterile ants
in their colony or kept them in isolation. The mortality after 36 h was
much lower for infected ants kept with their nestmates (22%) than for
infected ants kept in isolation (90%, least square means: DF = 154;
Z = −2.759; P = 0.02; Fig. 1c). By contrast, there was no signiﬁcant difference between the mortality of sterile ants kept with their nestmates
or in isolation (least square means: DF = 154; Z = 1.04; P = 0.89; Fig. 1c).
The mortality of infected ants was also not signiﬁcantly different than
the mortality of sterile ants when these individuals were kept with their
nestmates (least square means: DF = 154; Z = −0.630; P = 1; Fig. 1c).
Overall, these data demonstrate that M. analis workers are capable of
effectively treating wounds that have been exposed to soil pathogens
through social interactions.
By culturing the soil medium on agar plates, we were able to
isolate three potential pathogens (the endosymbiotic bacterium Burkholderia sp. and its fungal host Rhizopus microsporus, and the bacterium Pseudomonas aeruginosa, Fig. 1b and Supplementary Fig. 1c, d).
While the application of B. sp. and R. microsporus (separately or
together) on wounds did not signiﬁcantly decrease survival (Supplementary Figs. 2b and 3), the application of P. aeruginosa (OD = 0.05), a
bacterium widespread in various environments12, caused a mortality of
93% within 36 h (Fig. 2a). Since the treatment with only P. aeruginosa
showed a similar mortality as the treatment with all soil pathogens
(90%, Supplementary Fig. 3), we only used P. aeruginosa in subsequent
infection assays to better control pathogen load.
Like the experiments where the soil was applied to the wound,
the presence of nestmates was also effective in decreasing the
mortality of injured workers exposed to a known concentration of

To study the proximate mechanisms reducing mortality of ants
infected with P. aeruginosa when they are returned to their nestmates,
we introduced injured ants (with sterile and infected wounds) to their
nestmates and ﬁlmed them for 24 h. We observed that workers treated
the injury of infected ants by depositing secretions produced by the
metapleural gland (MG), which is located at the back of the thorax
(Fig. 3a). The MG secretions, which have antimicrobial properties13–17,
were applied in 10.5% of the wound care interactions (43 out of 411).
Before applying MG secretions, the nursing ant always groomed the
wound ﬁrst (i.e., “licking” the wound with their mouthparts). Nursing
ants then collected the secretions either from their own MGs (Supplementary Movie 1), by reaching back to the opening of the gland with
their front legs to collect the secretion and then licking their front legs
to accumulate it in their mouth, as described in other species15, or from
the MG of the injured ant itself, by licking directly into the gland’s
opening (Supplementary Movie 2). Wound care with MG secretions
lasted signiﬁcantly longer (85 ± 53 s) than wound care without MG
secretions (53 ± 36 s; t-test: DF = 47.92; t = −3.09; P = 0.003; Supplementary Fig. 4).
Remarkably, workers were able to discriminate between infected and sterile ants. Wound care treatment was provided more often
to infected ants upon initial introduction to the nest and again 10and 11-hours post-introduction (hierarchical generalized additive
model: P < 0.05; Fig. 3b). Moreover, MG secretions were deposited
signiﬁcantly more often on wounds of infected than sterile ants
between 10 and 12 h after infection (hierarchical generalized additive
model: P < 0.05; Fig. 3c). When treatment with MG secretions was
prevented (through plugging the MG opening of all ants in the subcolony), mortality of infected ants reached 100% within 36 h compared to only 33% when workers had access to the MG secretions
(least square means: DF = 47; Z = 2.99; P = 0.039; Fig. 4, Supplementary Fig. 5, and Supplementary Table 6). By contrast, the plugging of
the MG opening had no signiﬁcant effect on the survival of sterile
ants (least square means: DF = 47; Z = 0.02; P = 1; Fig. 4 and Fig. S5 and
Table S6).

Nature Communications | (2023)14:8446

CHC proﬁles as infection cues
Because cuticular hydrocarbons (CHCs) are known to be frequently
used as a source of information in ants18, we investigated whether the
CHC proﬁle of infected ants changed over the course of the infection.
Immediately after injury, infected ants did not differ from sterile ants
in their CHC proﬁle (ADONIS: R2 = 0.013; F(1,11) = 0.13; P = 0.96; Supplementary Tables 7 and 8). This proﬁle changed in both types of ants
during the two hours after injury (Sterile ants: ADONIS: R2 = 0.15;
F(1,16) = 2.74; P = 0.03; Infected ants: ADONIS: R2 = 0.13; F(1,17) = 2.42;
P = 0.04), converging towards a similar proﬁle for both types of ants
(ADONIS: R2 = 0.008; F(1,22) = 0.17; P = 0.87). Thereafter, the CHC proﬁle of infected ants remained unchanged until 11 h after injury (ADONIS: R2 = 0.063; F(1,23) = 1.48; P = 0.13), while the CHC proﬁle of sterile
ants changed signiﬁcantly (ADONIS: R2 = 0.14; F(1,22) = 3.54; P = 0.04),
thereby becoming signiﬁcantly different from the CHC proﬁle of
2

Article

https://doi.org/10.1038/s41467-023-43885-w

Fig. 1 | Lethal effects and diversity of soil pathogens. a Relative 16 S rRNA gene
copies (bacterial load ΔCq) for individuals whose wounds were exposed to a sterile
PBS solution (blue: Sterile), or soil pathogens diluted in PBS (red: Infected, OD = 0.1),
2 and 11 h after exposure (n = 10 per boxplot, see Supplementary Table 1 for statistical results). Signiﬁcant differences (P < 0.05) are shown with different letters and
were calculated using a two-sided least square means with Holm-Bonferroni correction. b Absolute 16 S rRNA gene copy numbers summarized at the genus-level for
the 18 amplicon sequence variants (ASV) that had at least 1% relative abundance in
ﬁve of the 40 analyzed ants across the experiment (n = 10 per boxplot). Signiﬁcance
is shown for pairwise comparisons between sterile (blue) and infected ants (red):
***=P < 0.001; **=P < 0.01; *=P < 0.05; n.s.= not signiﬁcant (P > 0.05). Detailed

statistical results in Supplementary Table 2, signiﬁcant differences were calculated
with a two-sided permutation t-test with Holm-Bonferroni correction. (c) Kaplan –
Meier cumulative survival rates of workers in isolation (dotted line) or inside the nest
(solid line) whose wounds were exposed to a sterile PBS solution (blue: sterile), or
soil pathogens diluted in PBS (red: infected, OD = 0.1). Signiﬁcant differences
(P < 0.05) are indicated with different letters. Detailed statistical results in Supplementary Fig. 2a and Supplementary Table 3, signiﬁcant differences were calculated
using a two-sided least square means with Holm-Bonferroni correction. Boxplots
show median (horizontal line), interquartile range (box), distance from upper and
lower quartiles times 1.5 inter-quartile range (whiskers), outliers (>1.5x upper or
lower quartile). Source data are provided as a Source Data ﬁle.

infected ants (ADONIS: R2 = 0.19; F(1,23) = 5.4; P = 0.007). Eleven hours
after injury, the CHC proﬁle of sterile ants had converged again to the
proﬁle at 0 h (ADONIS: R2 = 0.03; F(1,17) = 0.53; P = 0.45), while the CHC
proﬁle of injured ants remained signiﬁcantly different (ADONIS:
R2 = 0.19; F(1,17) = 3.82; P = 0.008).
Consistent with the idea that changes in CHC proﬁles could provide cues on the health status of ants9, the observed differences in the
CHC proﬁles mostly stemmed from differences in the relative abundance of alkadienes (Supplementary Fig. 6 and Supplementary

Table 9), which are among the CHC compounds most relevant for
communication in social insects19. These changes in the CHC proﬁle
are generally regulated by differentially expressed genes in the fat
bodies20. To identify the genes likely responsible for the observed CHC
changes between infected and sterile ants, we conducted transcriptomic analyses of the fat bodies of the same individuals. A total of
18 genes related to CHC synthesis were differentially expressed
between infected and sterile ants 11 h after exposure to P. aeruginosa
(in addition, 17 genes out of 378 that were differentially expressed were

Nature Communications | (2023)14:8446

3

Article

https://doi.org/10.1038/s41467-023-43885-w

Fig. 2 | Survival probability and pathogen load of sterile and infected ants.
a Kaplan–Meier cumulative survival rates of workers in isolation or inside the nest
whose wounds were exposed to P. aeruginosa diluted in PBS (Infected, OD = 0.05)
or a sterile PBS solution (Sterile). Detailed statistical results in Supplementary
Fig. 2c and Supplementary Table 3. b relative bacterial load (ΔCq) of Pseudomonas
at two different time points (2 h and 11 h) for ants in isolation or inside the nest with
wounds treated the same way as in Fig. 2a (Infected or Sterile). n = 6 per boxplot,
signiﬁcant differences (P < 0.05) are shown with different letters (Supplementary

Table 4). Boxplots show median (horizontal line), interquartile range (box), distance from upper and lower quartiles times 1.5 inter-quartile range (whiskers),
outliers (>1.5x upper or lower quartile). Signiﬁcant differences in both panels were
calculated using a two-sided least square means with Holm-Bonferroni correction.
Colors in both panels show in red: infected ants in isolation, in light blue: sterile ants
in isolation, in dark blue: sterile ants inside the nest, in green: infected ants inside
the nest. Source data are provided as a Source Data ﬁle.

immune genes; Fig. 5a and Supplementary Tables 10 and 11), while only
two genes related to CHC synthesis were differentially expressed two
hours after infection (in addition to 7 immune genes out of 164; Fig. 5b
and Supplementary Tables 10 and 11).

known to have antimicrobial properties27. There were also 14 carboxylic acids, making up 52% of the secretions content (Supplementary Fig. 8 and Supplementary Table 13), probably leading to a lower
pH detrimental to bacterial survival and growth13.
The number of chemical compounds identiﬁed in the MG’s
secretions of M. analis (112) is far greater than in other ant species,
where the number of compounds ranges between 1 and 35 and mostly
consists of carboxylic acids rather than alkaloids or antibiotic-like
compounds13. In other ant species, the compounds in MG secretions
are generally believed to be effective against early developmental
stages of pathogens, like during sterilization of the nest or as a
response to recent fungal exposure13–15. While we also observed a
prophylactic use of MG secretions directly after injury, the most
intense use of the MG was at the height of the infection (10–12 h,
Fig. 3c), a period where, without care, infected ants start to die (Fig. 2a).
This suggests that, in addition to prophylactic substances, the MG
secretions also contain antimicrobial compounds capable of combatting festering infections.
During wound care, the use of the MG secretions probably fulﬁls a
similar role as the mammals’ antiseptic saliva. This analogous role
likely led to the convergent evolution of functionally similar antimicrobial and wound healing proteins28. However, wound care treatments are indiscriminate in mammals as they have never been
observed to depend on the infected state of the wound.
This study reveals an effective behavioral adaptation to identify
and treat festering infections of open wounds in a social insect. The
prophylactic and therapeutic use of antimicrobial secretions to
counteract infection in M. analis (Fig. 3c) mirrors modern medical
procedures used for treating dirty wounds29. Remarkably, the primary
pathogen in ant’s wounds, Pseudomonas aeruginosa, is also a leading
cause of infection in combat wounds, where infections can account for
45% of casualties in humans30. In M. analis the targeted treatment with
antimicrobial compounds was extremely effective in preventing lethal

Antimicrobial content and efﬁcacy of the Metapleural gland
To quantify the capabilities of MG secretions to inhibit bacterial
growth, we conducted antimicrobial assays. The growth of P. aeruginosa was reduced by >25% when MG secretions were included in a
lysogeny broth (LB) solution compared to a control LB solution without the MG secretions (Mann-Whitney U test: W = 54,
P < 0.001, Fig. 3d).
Since P. aeruginosa has repeatedly developed antimicrobial
resistance21 and because most antimicrobial compounds found in
animal saliva are unable to inhibit the growth of P. aeruginosa22, we
conducted proteomic and chemical analyses of the MG secretions. The
proteomic analysis revealed 41 proteins (Supplementary Fig. 7 and
Supplementary Table 12), 15 of which showed molecular similarity to
toxins, which often have antimicrobial properties23. Five proteins had
orthologs known to have antimicrobial activity (e.g., lysozyme,
hemocytes, 2 MRJP1-like proteins) and three with melanization, a
process implicated in wound healing in insects24,25. There was no clear
function for nine proteins including the most abundant protein
(13 ± 16% of the MG’s endogenous protein content), for which no
ortholog could be found. The evolutionarily young gene coding this
protein could be a promising candidate for antimicrobial research,
with research into next-generation antibiotics often relying on antimicrobial peptides26. The gas-chromatography/mass-spectrometry
(GC-MS) analyses of the MG further revealed 112 organic compounds
(23 of which could not be identiﬁed, Supplementary Fig. 8 and Supplementary Table 13). Six of the identiﬁed compounds had antibioticand/or fungicide-like structures and 35 were alkaloids. While we could
not identify the exact structure of the alkaloids, many of them are

Nature Communications | (2023)14:8446

4

Article

https://doi.org/10.1038/s41467-023-43885-w

Fig. 3 | Use and efﬁcacy of the metapleural gland (MG) secretions during
wound care. a Micro CT scan showing the location of the MG. Blue: secretory cells;
yellow: atrium. b Probability of receiving wound care over 24 h ﬁtted with a hierarchical generalized (binomial) additive model (HGAM); shaded bars indicate
periods during which the probability of receiving care was signiﬁcantly (P < 0.05)
higher for infected (red; n = 6) than sterile (blue; n = 6) individuals. The line
represents the predicted probability by the HGAM, with the colored shaded area
representing the 95% conﬁdence interval. c Probability of receiving antimicrobial

wound care with metapleuralgland (MG) secretions (the same ants as in Fig. 3b),
modeled with identical HGAM speciﬁcations. d Bacterial growth assay for P. aeruginosa either in LB broth (positive control, n = 6) or LB broth with MG secretions
(Metapleural gland n = 9). Two-sided Mann-Whitney U test: W = 54, P < 0.001.
Boxplots show median (horizontal line), interquartile range (box), distance from
upper and lower quartiles times 1.5 inter-quartile range (whiskers), outliers (>1.5x
upper or lower quartile). Source data are provided as a Source Data ﬁle.

bacterial infections by P. aeruginosa. This could potentially lead to
promising new medical compounds to cure infections in human
societies.

agreement under research permit number N°018 / MINEDD /
OIPR / DZ.

Experimental design

Methods
The research conducted in this study complies with all relevant
ethical regulations and was approved by the park management of
Ofﬁce Ivoirien des Parcs et Réserves (OIPR) in Côte d’Ivoire as part of
the bilateral research agreement between Germany (represented by
the University of Würzburg) and Côte d’Ivoire (represented by OIPR).
The ants collected for this study are part of the bilateral research

Nature Communications | (2023)14:8446

The study was conducted in a humid savannah woodland located in the
Comoé National Park, northern Côte d’Ivoire (Ivory Coast), at the
Comoé National Park Research Station (8°46’N, 3°47’W)31. Experiments, observations, and sample collections in the Comoé National
Park were carried out from April to June 2018, February, April to June
and September to October 2019, April 2020 and October 2022.
Megaponera analis is found throughout sub-Saharan Africa from 25°S
5

Article

https://doi.org/10.1038/s41467-023-43885-w

100

+a a
+a

Survival probability [%]

75

50

Plugged MG
Open MG

+ Sterile
+ Infected

25

b

0
0

4

8

12

16

20

24

28

32

36

Time [h]
Fig. 4 | Effect of MG secretions on survival of sterile and infected ants.
Kaplan–Meier cumulative survival rates of workers inside sub-colonies with a
plugged metapleuralgland (MG) opening (dotted line) or with an unmanipulated
MG opening (solid line) whose wounds were exposed to a sterile PBS solution (blue,
sterile n = 12) or P. aeruginosa diluted in PBS (OD = 0.05) (red, infected n = 12).
Detailed statistical results in Supplementary Fig. 2d and Supplementary Table 6,
signiﬁcant differences were calculated using a two-sided least square means with
Holm-Bonferroni correction. Additional data for ants in isolation can be found in
Supplementary Fig 5. Source data are provided as a Source Data ﬁle.

to 12°N32 and known to show monophasic allometry within its worker
sizes33. We thus divided the workers into majors (head width > than
2.40 mm), minors (head width <1.99 mm), and intermediates (head
width 2.40 − 1.99 mm). All experiments were carried out on the minor
caste, the individuals most frequently injured2. All ﬁeld studies were
conducted in accordance with local legislation and permission by the
Ofﬁce Ivoirien des Parcs et Réserves (OIPR).

Laboratory colonies
Eleven colonies, including queen and brood (colony size 1083 ± 258
ants), were excavated and placed in artiﬁcial nests in the ﬁeld stations
laboratory. PVC nests (30x20x10 cm) were connected to a 1x1m
feeding arena. The ground surface was covered with soil from the
surrounding area (up to a height of 2 cm). Colonies were fed by placing
in the feeding arena Macrotermes bellicosus termites collected from the
surrounding area. These termites were found by scouts and triggered
raiding behavior. The laboratory windows were kept open to maintain
a natural humidity, temperature, and day-night cycle (light regime).

Survival of injured ants
To quantify the lethality of various types of pathogens, workers were
injured by a sterile cut in the middle of the femur on the hind leg and
had the fresh wound submerged for 2 s in a 10 μL phosphate-buffered
saline (PBS) solution with a known pathogen concentration (~106
bacteria of either the mix of soil pathogens or isolated pathogen).
Afterwards the injured ants were placed inside a cylindrical glass
container with a diameter of 3 cm and a height of 5 cm. Before the
experiments, the glass containers were ﬁlled with 1 cm of surface soil
and placed for 3 h at 220 °C in an oven together with the forceps and
scissors for sterilization. Nest-like humidity was created by moistening
the soil with 1 mL of sterilized water (boiled for 10 min) and covered
with aluminum foil. The isolation experiments were conducted at 24 °C

Nature Communications | (2023)14:8446

in a sterile room. To test for mortality, the injured ant was checked
upon once per hour for the next 36 h, if no reaction was observed even
after shaking the container the ant was classiﬁed as dead. To ensure
replicability of the survival experiments a negative (sterile PBS solution) and positive control (using a sterile PBS solution with 0.1 optical
density (OD) of Pseudomonas aeruginosa (PSE)) were always included
during each survival experiment. The sample sizes were PBS: n = 104;
PSE 0.1: n = 61; PSE 0.05: n = 15 (Fig. 2a and Supplementary Fig. 3). For
all sample sizes and statistics see Supplementary Fig. 2.
To test if wound care by nestmates reduces the mortality of
infected ants, we ﬁrst marked 10 ants per colony during a raid. 24 h
later, all marked ants were injured in the same way as in the isolation
trial. Afterwards, the wound of the injured ant was either exposed to
a sterile PBS solution (sterile), a sterile PBS solution containing 0.05
OD of P. aeruginosa or a sterile PBS solution containing 0.1 OD of
surface soil pathogens (grown on agar plates). Isolation trials with
the same treatments were always conducted in parallel. Sample size
of in nest survival experiments: PBS: n = 12; PSE 0.05: n = 12; Soil
n = 18 (Figs. 1 and 2a). For all sample sizes and statistics see Supplementary Fig. 2.
To test the importance of the Metapleural gland (MG) secretions
we divided three colonies (~1000 ants) into two equally sized subcolonies including brood. The queens were left with a small retinue of
20 workers in a separate container. For each colony we plugged the
MG opening of all workers of one of the sub-colonies with acrylic color,
while for the other sub-colony acrylic color was placed on the thorax of
the workers. Afterwards the experimental procedure was identical to
the wound care experiment described above. Eight individuals were
removed from each sub-colony, marked, and wounded and the wound
exposed to either a sterile PBS solution (n = 4 per sub-colony) or a
sterile PBS solution containing 0.05 OD of P. aeruginosa (n = 4 per subcolony). Sample sizes were thus in total: Sterile: n = 12; Infected: n = 12
for both treatments (with and without plugged MG opening; Fig. 4). In
addition, to quantify any effects of the plugging of the MG opening on
individuals, a parallel experiment with n = 12 Sterile and n = 12 Infected
ants with or without plugged MG opening was run in isolation (Supplementary ﬁg. 5 and Supplementary Table 6).
The pathogen concentration was measured by optical density
(OD) using a portable Ultrospec 10 cell density meter (Biochrom) with
sterile PBS as solvent. For the soil pathogens, we collected surface soil
in the surrounding area of the nest and grew it over 36 h on agar plates
(Supplementary Fig. 1c). For P. aeruginosa we created cultures of the
isolated strains (Supplementary Fig. 1d) in the ﬁeld lab from frozen
samples kept in Tryptic Soy Broth (TSB) medium with 25% glycerol
(stored and transported at −23 °C). After replating the bacterial culture
once on a fresh plate, we waited 16 h before applying the pathogen on
fresh wounds. For all experiments, we used trypticase soy agar (TSA)
plates to culture the bacteria.

Treatment of wounds by nestmates
To quantify the wound care behaviors inside the nest, we ﬁlmed the
ants using a Panasonic HC-X1000 and analyzed the videos using VLC
media player v.3.0.16 Vetinari (intel 64 bit). The wound of the injured
ant was either exposed to a sterile PBS solution (Sterile) or a sterile PBS
solution containing 0.05 OD of P. aeruginosa (Infected).
All manipulated ants were placed in front of the nest entrance
directly after a raid and the nest was ﬁlmed for the subsequent 24 h.
Only one trial was conducted per colony with a total of four injured
ants per colony (two sterile and two infected), in a total of three
colonies (i.e., n = 6 infected ants and n = 6 sterile ants, Fig. 3b, c). The
observed wound care behaviors were classiﬁed into two categories:
(1) wound care: a nestmate cleans the open wound with its mouthparts;
(2) metapleural gland (MG) care: a nestmate collects MG secretions
either from its own gland (Supplementary Movie 1) or from the injured
ant (Supplementary Movie 2) in its mouth before caring for the wound.
6

Article

https://doi.org/10.1038/s41467-023-43885-w

Fig. 5 | Differential gene expression between sterile and infected ants 2 and 11 h
after injury. Infection triggers changes in the expression of hundreds of genes.
Volcano plot illustrates the fold up- and down-regulation of immune-related genes
(red triangles, Supplementary Table 11) and lipids and CHC-related genes (yellow
squares, Supplementary Table 10), 11 h (a) and 2 h (b) after infection. Positive
Log2FoldChange values correspond to genes up-regulated in infected ants when

compared to sterile ants, while negative values are down-regulated in infected ants.
Signiﬁcant differences were calculated using a two-sided Wald test and corrected
for multiple testing using the Benjamini and Hochberg method (genes with signiﬁcant differential expression are marked in blue, i.e. adjusted P-value < 0.05).
Source data are provided as a Source Data ﬁle.

These behaviors were quantiﬁed for the ﬁrst 24 h and summarized in
10 min intervals.

kept in isolation were further observed for a total of 36 h to ensure that
the survival curves in this experiment resembled those in Fig. 2a.

Sample collection protocol for chemical, genetic, and microbial
analyses

Pathogen identiﬁcation & isolation

To quantify the cuticular hydrocarbon (CHC) proﬁle (Supplementary
Fig. 6), differential gene expression (Fig. 5), and pathogen load in the
thorax content (Figs. 1a and 2b), we used the same experimental design
as used to quantify the survival of injured ants (see above). In total,
30 sterile and 30 infected ants (P. aeruginosa OD = 0.05) were prepared, 12 sterile and 12 infected ants were kept in isolation, another
12 sterile and infected ants were placed inside the nest and 6 sterile and
6 infected ants were collected immediately after injury. Sterile or
infected ants were then collected from their enclosures at either 2 or
11 h after manipulation (n = 6 per treatment). Only ants that did not die
until these time points were used for further analyses. The collected
ants were then ﬁrst placed in hexane for 10 min to extract the CHC
proﬁle. The gaster was then cut off and placed in RNAlater for genetic
analyses and the thorax was placed in 100% ethanol for the microbial
analyses. The samples for genetic and microbial analyses were brought
to the University of Lausanne and the samples for chemical analyses to
the University of Würzburg. Another 10 sterile and 10 infected ants

Nature Communications | (2023)14:8446

To isolate potential pathogens, we collected surface soil in the surrounding area of the nest and grew its water extract on Sarborough
Dextrose Agar (SDA) plates for 36 h at local temperature to get the ‘soil
pathogen mix’ (Supplementary Fig. 1c). Most of the plate was at ﬁrst
overgrown by black mycelia identiﬁed as Rhizopus microsporus. This
fungus is known to contain symbiotic bacteria Burkholderia sp. (species not identiﬁed), as we conﬁrmed by Sanger sequencing and bacterial community analysis (Fig. 1c). After several days, the culture
started to show several large colonies of slimy microorganisms that
were isolated and identiﬁed as Pseudomonas aeruginosa (Supplementary Fig. 1d). For infection assays (Supplementary Fig. 3), we isolated
Burkholderia and its fungal host Rhizopus from each other by repeated
passaging with antifungal nystatin (0.25 μl/ml in M9 agar, 30 °C) or
antibacterial ciproﬂoxamin (0.02 mg/ml in SDA). No representatives of
Klebsiella were isolated.
The identiﬁcation of all microorganisms was done by preparation
of a PCR-ready DNA from a piece of biomass as described in Lõoke
et al.34. PCR reactions were done with universal fungal primers ITS5 5’-T
7

Article
CCTCCGCTTATTGATATGC-3′ and ITS4 5′-GGAAGTAAAAGTCGTAAC
AAGG-3′35 (GoTaq polymerase, Tm = 55 °C, elongation for 40 s) and
commonly used universal bacterial primers 27F 5′-AGRGTTYGAT
YMTGGCTCAG-3′ and 1492 R 5′-GGTTACCTTGTTACGACTT-3’ (same
protocol but elongation for 1 min 20 s). The presence of PCR amplicons was veriﬁed on electrophoresis gel and the fragments were sent
for Sanger sequencing. The resulting sequences were then screened
against NCBI database with nucleotide BLAST36.

Microbiome analysis and bacterial load quantiﬁcation
To quantify the composition of the bacterial community in workers,
we used the Powersoil DNA isolation kit (MO Bio) to extract DNA from
M. analis thoraxes kept in RNAlater. Samples were homogenized by
vortexing for 10 min, followed by two times 45 sec bead beating at 6 m/
s using a FastPrep24TM 5 G homogenizer. Then we continued according
to the kit’s manual and eluted DNA in 100 µL of nuclease-free water.
Bacterial loads were quantiﬁed with a QuantStudio5 qPCR
instrument (Applied Biosystems) using the reaction set-up described
in Kešnerová et al.37 and the thermal cycling conditions recomm
ended for SYBR® Select Master Mix. For quantifying total bacterial
loads, we used our designed primers #1047 5’-AGGATTAGAT
ACCCTRGTAGTC-3’ and #1049 5’-CATSMTCCACCRCTTGTGC-3’ (at
doubled 0.4 µM concentration). For speciﬁc targeting we used P.
aeruginosa primers #1209 5’-GTAGATATAGGAAGGAACACCAG-3’ and
#1210 5’-GGTATCTAATCCTGTTTGCTCC-3’ and for normalization to
the host’s housekeeping gene we used M. analis 28S rRNA gene primers #1207 5’-CTGCCCGGCGGTACTCG-3’ and #1208 5’-A
CCGGGGACGGCGCTAG-3’. Serial dilutions (10x) showed that these
primers performed with an ampliﬁcation efﬁciency (E) of 1.86
(R2 = 0.99), 2.00 (R2 = 0.99), and 1.87 (R2 = 0.99), respectively.
Total bacterial (Fig. 1a) and P. aeruginosa (Fig. 2b) 16 S rRNA gene
(target) copy numbers were expressed relatively to M. analis 28 S rRNA
gene copy numbers (host) based on the following equation: ΔCq=
2^(Cqhost - Cqtarget), where Cq was the measured ‘quantiﬁcation cycle’
value. To calculate the total 16 S rRNA gene copy number in 1 μl of each
DNA sample that was used for absolute bacterial abundance based on
ASV counts (Fig. 1b) we used the equation n = E^(intercept – Cq)38,
where standard curve’s intercept = 38.2337.

16 S rRNA gene amplicon-sequencing
16 S rRNA gene amplicon sequencing data were obtained from 40
experimental samples, a mock sample (to verify consistency of the
MiSeq run compared to previous studies in our group), two blank
DNA extractions and a negative PCR control with only H2O. We followed the Illumina 16 S metagenomic sequencing preparation guide
(https://support.illumina.com/documents/documentation/chemistry_
documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.
pdf) to amplify and sequence the V4 region of the 16 S rRNA gene.
Primers for the ﬁrst PCR step were 515F-Nex (TCGTCGGCAGCGTCAG
ATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA) and 806R-Nex
(GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGG
TWTCTAAT). PCR ampliﬁcations were performed in a mix of 12.5 μL of
Invitrogen Platinum SuperFi DNA Polymerase Master Mix, 5 μL of
MilliQ water, 2.5 μL of each primer (5 μM), and 2.5 μL of template DNA.
PCR conditions were 98 °C for 30 s, 25 cycles of 98 °C for 10 s, 55 °C for
20 s, and 72 °C for 20 s, and a ﬁnal extension step at 72 °C for 5 min.
Ampliﬁcations were conﬁrmed by 2% agarose gel electrophoresis. The
PCR products were then puriﬁed with Clean NGS puriﬁcation beads
(CleanNA) in a 1:0.8 ratio of PCR product to beads, and eluted in
27.5 μL of 10 mM Tris, pH 8.5. We then performed a second PCR step in
which unique dual-index combinations were appended to the amplicons using the Nextera XT index kit (Illumina). Second-step PCRs were
performed in a 25 μL, using 2.5 μL of the PCR products, 12.5 μL of
Invitrogen Platinum SuperFi DNA Polymerase Master Mix, 5 μL of
MilliQ water, and 2.5 μL of each of the Nextera XT indexing primers 1
Nature Communications | (2023)14:8446

https://doi.org/10.1038/s41467-023-43885-w

and 2. PCR conditions were 95 °C for 3 min followed by eight cycles of
30 s at 95 °C, 30 s at 55 °C, and 30 s at 72 °C, and a ﬁnal extension step
at 72 °C for 5 min. The libraries were again puriﬁed using Clean NGS
puriﬁcation beads in a 1:1.12 ratio of PCR product to beads, and eluted
in 27.5 μL of 10 mM Tris, pH 8.5. The amplicon concentrations,
including the negative PCR control, mock, and blanks, were quantiﬁed
by PicoGreen and pooled in equimolar concentrations (except for the
water and blank samples that were kept at lower concentrations). We
veriﬁed that the ﬁnal pool was of the right size using a Fragment
Analyzer (Advanced Analytical). MiSeq (Illumina) sequencing was then
performed at the Genomic Technology Facility of the University of
Lausanne, producing (2 × 250 bp) reads. We obtained a total of 979’126
paired-end reads across the 40 experimental samples.

Analyses of 16 rRNA gene amplicon-sequencing data
Raw sequencing data (deposited at the Sequence Read Archive (SRA)
under PRJNA826317) were analyzed with the Divisive Amplicon
Denoising Algorithm 2 (DADA2) package v.1.20.0 in R. All functions
were run using the recommended parameters (https://benjjneb.
github.io/dada2/tutorial.html) except for the ﬁltering step in which
we truncated the forward and reverse reads after 232 and 231 bp,
respectively. At the learnErrors step, we then set randomize=TRUE and
nbases=3e8. Amplicon-sequence variants (ASVs) were classiﬁed with
the SILVA database (version 138). Unclassiﬁed ASVs and any ASV
classiﬁed as chloroplast, mitochondria or Eukaryota were removed
with the “phyloseq” package version 1.36.0, using the “subset taxa”
function. We then used the “prevalence” method in the R package
“decontam” v.1.12.0 to identify and remove contaminants introduced
during laboratory procedures, using the negative PCR control and the
blank samples as reference. This procedure ﬁltered out three contaminant ASVs. Four additional ASVs were removed as they clearly
represented (low abundance) contaminants due to index swapping
from samples of a different project that were sequenced in the same
sequencing run. We then calculated absolute bacterial abundances of
each ASV by multiplying the proportion of each ASV by the total 16S
rRNA gene copy number of each sample as measured by qPCR. To
assess differences in community structure between treatments we ran
ADONIS tests after calculating Bray-Curtis dissimilarities with the
absolute ASV abundance matrix and plotted ordinations based on
these Bray-Curtis dissimilarities (Supplementary Fig. 1a).
To test for differences in absolute abundance of individual bacterial genera between sterile and infected ants (Supplementary Fig. 1b),
we used a permutation approach (permutation t-test) as done in Kešnerová et al.39. To do this, we selected the ASVs that had at least 1%
relative abundance across ﬁve samples (18 ASVs belonging to 11 genera). We then calculated copy numbers at the genus-level for each of
the 40 samples. At each time-point (2 h and 11 h), we randomized the
values of the calculated copy numbers for each genus 10,000 times
and computed the t values for the tested effect for each randomized
dataset. The P values corresponding to the effects were calculated as
the proportion of 10,000 t values that were equal or higher than the
observed one.

Chemical analysis of cuticular hydrocarbons
To quantify differences in CHC proﬁles between infected and sterile
ant workers, cuticular hydrocarbon extracts were evaporated to a
volume of approximately 100 μL and 1 μL was analyzed by using a
6890 gas chromatograph (GC) coupled to a 5975 mass selective
detector (MS) by Agilent Technologies (Waldbronn, Germany). The GC
was equipped with a DB-5 capillary column (0.25 mm ID × 30 m; ﬁlm
thickness 0.25 μm, J & W Scientiﬁc, Folsom, Ca, USA). Helium was used
as a carrier gas with a constant ﬂow of 1 mL/min. A temperature program from 60 °C to 300 °C with 5 °C/min and ﬁnally 10 min at 300 °C
was employed. Mass spectra were recorded in the EI mode with an
ionization voltage of 70 eV and a source temperature of 230 °C. The
8

Article
software ChemStation v. F.01.03.2357 (Agilent Technologies, Waldbronn, Germany) for windows was used for data acquisition. Identiﬁcation of the components was accomplished by comparison of library
data (NIST 17) with mass spectral data of commercially purchased
standards and diagnostic ions.
To compare the relative abundances of the different compound
groups (Supplementary Fig. 6), all compounds were identiﬁed (Supplementary Table 8) and grouped either into Alkanes, Alkenes, Alkadienes or Methyl-branched alkanes for each individual.

Antimicrobial assay
To assess the antimicrobial efﬁcacy of MG extracts (Fig. 3d), we
quantiﬁed the increase over time of the OD in a 96-well plate box, with
the outer row ﬁlled with 70 μL of PBS. In total we did 3 replicates per
sample with a total of 70 μL per sample. Negative control: 70 μL LuriaBertani (LB) broth (n = 6). Positive control: 68 μL LB broth +
2 μL P. aeruginosa (n = 6). MG sample: 66 μL LB broth + 2 μL P. aeruginosa + 2 μL MG sample (n = 9).
For the preparation of the pathogen sample, we plated P. aeruginosa from a frozen stock on TSA plates. After 24 h the bacterial
culture was replated. After 12 h aliquots were created using the new
bacterial culture in a ﬂask containing 15 mL of freshly sterilized LB
broth for an initial OD reading. Afterwards the ﬂask was placed in an
incubator shaker (Amerex Steadyshake 757) at 180 rpm, 30 °C for the
bacteria to grow. Once the OD readings were between 02–0.5 OD (the
exponential growth phase) the ﬂask was put on ice until the experiments started.
For the preparation of the MG sample, we pooled 10 MGs in 1.5 mL
Eppendorf-Cups. We then froze the samples in liquid nitrogen and
crushed the sample material using sterile pellets. We then added 50 μL
of PBS-Buffer, vortexed shortly before centrifuging the sample for
5 min at 3000 x g at 4 °C. We then extracted 30 μL of the supernatant
into a new cup and repeated the centrifugation process. Afterwards
20 μL of the supernatant was placed again in a new cup and stored at
−20 °C. Preparation of the wells was conducted on ice at 4 °C to prevent bacterial growth and degradation of the MG samples. The antimicrobial assays were done in a microplate reader (Synergy H1 BioTek)
at 30 °C with a 600 nm wavelength for 8 h with a double orbital
shaking step after each OD reading cycle (every 10 min) at the University of Lausanne.
To calculate the intrinsic growth rate of the microbial population
(r) in Fig. 3d we used the package growthcurver (v. 0.3.1) with the
statistical software R v4.1.0. “r” represents the growth rate that would
occur if there were no restrictions imposed on total population size.

Sample collection for metapleural gland extracts
Due to the difﬁculty of collecting adequate amounts of metapleural
gland secretions from the gland’s atrium (it is a very sticky substance
that adheres to the cuticle), we decided to remove the atrium of the
gland together with the secretory cells entirely, using a microscalpel
and microscissors. To avoid using a solvent we used a Thermodesorber
unit coupled to a GC-MS (TD-GC-MS). For this we transported frozen
ants to the University of Würzburg and did the extractions directly in
the laboratory next to the TD-GC-MS. One metapleural gland from
each of six worker was pooled per sample. As a control we further
collected pieces of cuticle of similar size from the side of the thorax to
identify any potential contaminations which might have occurred
during dissections. In total 3 samples (of 6 individuals each) were
analyzed (Supplementary Fig. 8, Supplementary Table 13).

Chemical analysis of the metapleural gland
The MG samples were placed in a glass-wool-packed thermodesorption tube and placed in the thermodesorber unit (TDU; TD100-xr,
Markes, Offenbach am Main, Germany). The thermodesorption tube
was heated up to 260 °C for 10 min. The desorbed components were

Nature Communications | (2023)14:8446

https://doi.org/10.1038/s41467-023-43885-w

transferred to the cold trap (5 °C) to focus the analytes using N2 ﬂow in
splitless mode. The cold trap was rapidly heated up to 310 °C at a rate
of 60 °C per minute, held for 5 min and connected to the GC-MS
(Agilent 7890B GC and 5977 MS, Agilent Technologies, Palo Alto, USA)
via a heated transfer line (300 °C). The GC was equipped with an HP5MS UI capillary column (0.25 mm ID × 30 m; ﬁlm thickness 0.25 μm, J
& W Scientiﬁc, Folsom, Ca, USA). Helium was the carrier gas using
1.2874 ml/min ﬂow. The initial GC oven temperature was 40 °C for
1 min, then raised at a rate of 5 °C per min until reaching 300 °C, where
it was held for 3 min. The transfer line temperature between GC and
MS was 300 °C. The mass spectrometer was operated in electron
impact (EI) ionization mode, scanning m/z from 40 to 650, at 2.4 scans
per second. Chemical compounds were identiﬁed using the same
protocol as for the CHCs.

Proteomic analysis and sample preparation of the
metapleural gland
To characterize the proteins secreted by the metapleural gland, we
analyzed the proteome of the atrium of the gland, the metapleural
gland secretory cells and the hemolymph (Supplementary Fig. 7). To
be considered a metapleural gland protein that could mediate antimicrobial activity, proteins had to be found in the gland’s atrium, and
must have a higher abundance in the gland’s atrium than in the
hemolymph. The samples for proteomic analysis were collected in
April 2020 from workers of ﬁeld colonies collected in the Comoé
National Park. Three types of samples were collected: Secretory: dissected secretory cells without the atrium, (n = 6 samples, each pooled
from ﬁve dissections); Hemolymph: hemolymph was collected from
the thorax by a glass microcapillary through a small wound
(n = 6 samples, each pooled over ﬁve individuals, yielding 4–5 μL);
Atrium: due to the difﬁculty of collecting pure MG content we chose to
ﬁrst widen the opening of the atrium and add 1 μl of PBS before
extracting the content together with the PBS (n = 6 samples, each
pooled from ﬁve individuals). The samples were kept at −23 °C in Lobind Eppendorf tubes with 5 μl of PBS together with a 1x Protease
inhibitor Cocktail (Sigmafast) during transportation. Once in Lausanne, Switzerland, the samples were kept at −80 °C and processed
swiftly.
Proteins were digested according to a modiﬁed version of the iST
protocol40. Samples were resuspended in 20 μL of modiﬁed iST buffer
(1% sodium deoxycholate, 10 mM DTT, 100 mM Tris pH 8.6) and
heated at 95 °C for 5 min. They were then diluted with 24 μL of 4 mM
MgCl2 and 1:100 of benzonase in H2O and incubated 15 min at ambient
temperature. 14 μL of 160 mM chloroacetamide (in 10 mM Tris pH 8.6)
were then added and cysteines were alkylated for 45 min at 25 °C in the
dark. After addition of 0.5 M EDTA (3 mM ﬁnal concentration), samples
were digested with 0.1 μg of trypsin/Lys-C mix (Promega) at 37 °C for
1 h, followed by a second enzyme addition (0.1 μg trypsin/LysC) and 1 h
incubation. Two volumes of isopropanol + 1% triﬂuoroacetic acid (TFA)
were added to one volume of sample and loaded onto an equilibrated
OASIS MCX uElution plate (Waters) preﬁlled with SCX0 buffer (20%
MeCN, 0.5% formic acid) and centrifuged. The columns were washed
three times with 200 μL isopropanol + 1% TFA and once with 200 μL
HPLC solvent A (2% MeCN, 0.1% formic acid). The peptide mixture was
then sequentially eluted with 150 μL SCX125 buffer (20% MeCN, 0.5%
formic acid, 125 mM ammonium acetate), 150 μL SCX500 buffer (20%
MeCN, 0.5% formic acid, 500 mM ammonium acetate), and lastly with
150 μL basic elution buffer (80% MeCN, 19% water, 1% NH3).
Tryptic peptides fractions were dried and resuspended in 0.05%
triﬂuoroacetic acid, 2% (v/v) acetonitrile, for mass spectrometry analyses. Tryptic peptide mixtures were injected on an Ultimate RSLC
3000 nanoHPLC system (Dionex, Sunnyvale, CA, USA) interfaced to an
Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientiﬁc, Bremen, Germany). Peptides were loaded onto a trapping microcolumn
Acclaim PepMap100 C18 (20 mm × 100 μm ID, 5 μm, 100 Å, Thermo
9

Article
Scientiﬁc) before separation on a reversed-phase custom-packed
nanocolumn (75 μm ID × 40 cm, 1.8 μm particles, Reprosil Pur, Dr.
Maisch). A ﬂow rate of 0.25 μL/min was used with a gradient from 4 to
76% acetonitrile in 0.1% formic acid (total time: 65 min). Full survey
scans were performed at a 120’000 resolution, and a top-speed precursor selection strategy was applied to maximize acquisition of
peptide tandem MS spectra with a maximum cycle time of 0.6 s. HCD
fragmentation mode was used at a normalized collision energy of 32%,
with a precursor isolation window of 1.6 m/z, and MS/MS spectra were
acquired in the ion trap. Peptides selected for MS/MS were excluded
from further fragmentation during 60 s.
Tandem MS data were processed by the MaxQuant software
(version 1.6.14.0)41 using the Andromeda search engine42 matching to a
custom-made protein database containing 19’618 sequences of M.
analis (July 2019 version, see section “Genome annotation” for details),
supplemented with sequences of common contaminants. Trypsin
(cleavage at K,R) was used as the enzyme deﬁnition, allowing 2 missed
cleavages. Mass tolerance was 4.5 ppm on precursors (after recalibration) and 0.5 Da on MS/MS fragments. Only peptides with a minimal
length of 7 were considered for protein identiﬁcations. Carbamidomethylation of cysteine was speciﬁed as a ﬁxed modiﬁcation.
N-terminal acetylation of protein and oxidation of methionine were
speciﬁed as variable modiﬁcations. All identiﬁcations were ﬁltered at
1% FDR at both the peptide and protein levels with default MaxQuant
parameters. For protein quantitation the iBAQ values43 were used.
MaxQuant data were further processed with Perseus software (version
1.6.14.0)44 for ﬁltering, removing contaminants and proteins identiﬁed
with only 1 peptide, log2-transformation, normalization of values, and
ortholog annotations.
To determine which proteins are secreted by the MG, we proceeded through a series of ﬁlters. Proteins not found in the atrium
samples were eliminated and those found only in the MG atrium were
considered hits. The log2 fold change between the atrium and the
hemolymph were determined for all remaining proteins. Only proteins
that were on average 1.5-fold more abundant in atrium samples than
hemolymph were retained as hits. Percent of total iBAQ per sample was
assigned to each protein to indicate abundance. Visualization of
heatmaps was performed in Matlab 2020b (clustergram function). To
determine whether our selected proteins were toxin-like, we ran
clantox45 (http://www.clantox.cs.huji.ac.il/). Annotations (function,
orthology depth, and implications for wound healing) found in Supplementary Table 12 were determined using protein BLAST with the
experimental clustered nr database46.
The mass spectrometry proteomics data are deposited at the
ProteomeXchange Consortium via the PRIDE partner repository with
the dataset identiﬁer PXD033003.

Genome sequencing and assembly
The genome of M. analis was sequenced and assembled by The Global
Ant Genomics Alliance (GAGA, antgenomics.dk)47. To identify repeats
in the genome assembly, we used the package RepeatModeler v2.0,
which combines three de-novo repeat ﬁnding programs (RECON,
RepeatScout and LtrHarvest/Ltr_retriever)48. RepeatModeler ﬁrst
probes chunks of the genome assembly to ﬁnd repeats, then clusters
and classiﬁes identiﬁed repeats, producing a high-quality library of
consensus sequences of repeated sequence families. The consensus
sequences were used with RepeatMasker to softmask (in lower-case)
all repeats in the genome assembly.

Genome annotation
To annotate the M. analis genome assembly we used the ab-initio gene
predictors Augustus v3.3.349 and genemark50 within the BRAKER2
pipeline v2.1.451,52. Both tools were trained using RNA-seq reads (see
below) aligned to the softmasked version of the genome with STAR
v2.753 and protein evidence from NCBI refseq predictions for

Nature Communications | (2023)14:8446

https://doi.org/10.1038/s41467-023-43885-w

Acromyrmex echinatior, Atta colombica, Camponotus ﬂoridanus, Dinoponera quadriceps, Linepithema humile, Monomorium pharaonis,
Ooceraea biroi, Pogonomyrmex barbatus, Solenopsis invicta, Temnothorax curvispinosus, Vollenhovia emeryi and Wasmannia auropunctata. To enable functional interpretation of experimental data,
we annotated gene products. First, we identiﬁed gene orthologs in
Drosophila melanogaster to leverage the excellent quality of the
functional annotation in this species. To identify the orthologs, we
applied a reciprocal best blast approach using Orthologr54 between the
longest protein isoform for each gene from both species55. Additionally, we identiﬁed orthologs with Apis mellifera and Camponotus ﬂoridanus NCBI RefSeq gene annotations. Finally, we ran InterProScan
(version 5.30–69.0 and panther-data-14.1) on all proteins using default
settings. To assign functional information to M. analis annotated
genes, we primarily used the gene ontology of D. melanogaster
orthologs, but also results from sequence homology search on Interproscan and the Uniprot database.
To study the effect of infections on gene expression (Fig. 5), we
conducted a transcriptomic analysis. We dissected the gaster from the
same infected (soil OD = 0.1, n = 20) and sterile ants (n = 20), whose
thorax were used for the microbiome analyses in Fig. 1a, b. Samples
were collected 2 and 11 h after manipulation (n = 10 per timepoint) in
the Comoé ﬁeld research station and had the sting together with the
venom reservoir removed. The gaster was dissected without solvent
and stored in a RNAlater stabilisation solution at −23 °C (ThermoFisher
Scientiﬁc) for later analyses at the University of Lausanne. Afterwards,
the samples were homogenized with ceramic beads in 1 ml of Trizol
reagent. Homogenized samples were incubated for 5 min at room
temperature (RT) in Trizol, before adding Chloroform (200 μL). Samples were incubated for 5 min at RT then centrifuged (25 s at
12,000 rpm and 4 °C) and the upper aqueous layer (~500 μL) transferred to a new tube. We added Isopropanol (650 μL) and Glycogen
blue (1 μL; RNAse-free, Invitrogen, 15 mg/mL, #AM9516) then vortexed
and incubated overnight at −20 °C. To purify the RNA, we used a EtoH
precipitation method: samples were centrifuged (30 s at full speed at
4 °C), the supernatant was discarded and EtOH (1 mL at 80%) added.
We repeated this step a second time with EtOH (1 mL at 70%). Finally,
the supernatant was removed and the pellet, after a brief air dry
(15–20 s) at RT, was resuspended in nuclease-free water. Libraries were
prepared with the KAPA Stranded mRNASeq Library Preparation Kit
(#KK8421) according to the manufacturer’s protocol. Paired end
sequencing was performed on an Illumina Hiseq4000 sequencer at the
Genomic Technology Facility of the University of Lausanne. We
obtained ~15–25 million PE reads per individual. Sequence reads have
been deposited in NCBI Sequence Read Archive (SRA) under the
accession number PRJNA823913.
We mapped RNA-seq reads on the softmask genome assembly
using STAR v2.7 with the 2-pass mapping option and using the gene
annotations to properly ﬁnd exonic junctions53. Then, we used the
program featureCounts from the Subread package to count the
number of reads mapped on each exon for each gene56. Then using the
count’s ﬁles, we performed differential gene expression analyses using
DESeq257 of sterile and infected workers collected at 2 and 11 h after
treatment. P values of differential expression analyses were corrected
for multiple testing with a false discover rate (FDR) of 5%.

X-Ray Micro-CT imaging
To examine the internal morphology of the MG (Fig. 3a), we scanned
and visualized an intermediate-sized worker (CASENT0744096),
although multiple other workers of different sizes were scanned and
visually inspected to conﬁrm the morphology of the exemplar specimen was representative. The specimens were ﬁxed in 90% ethanol,
stained in 2 M iodine solution for a minimum of 7 days, then washed
and sealed in a pipette tip with 99% ethanol for scanning. The scans
were performed with the ZEISS Xradia 510 Versa 3D X-ray microscope
10

Article
at the Okinawa Institute of Science and Technology Graduate University, Japan. We scanned the thorax (mesosoma) and whole body to
visualize both the structure of the gland, and its location in the whole
ant. The scan parameters for the thorax scan were: Voxel size,
4.404 μm; Exposure time, 6 s; Voltage, 40 kV; Power, 3 W; Source distance, 23.147 mm; Objective, 4x; Projections, 1601. For the full body
scan, we used: Voxel size, 12.5 μm; Exposure time, 1 s; Voltage, 40 kV;
Power, 3 W; Source distance, 25.03 mm; Objective, 0.4x; Projections,
1601. The 3D reconstruction was performed using the ZEISS Scout-andScan Control System Reconstructor software (ZEISS Microscopy, Jena,
Germany). The MG structures were manually segmented using Amira
(version 2019.2; Thermo Fisher Scientiﬁc, Berlin, Germany) and
visualized using VGStudio3.4 (Volume Graphics GmbH, Heidelberg,
Germany). The gland atrium was rendered using isosurface and a
clipping plane was created to visualize the cross-section. All other
materials were visualized using Phong volume rendering.

Statistical analysis
For statistical analyses and graphical illustration, we used the statistical
software R v4.1.058 with the user interface RStudio v1.4.1717 and the R
package ggplot2 v3.3.559. To select the appropriate statistical tests, we
tested for deviations from the normal distribution with the
Shapiro–Wilks test (P > 0.05). A Bartlett test was used to verify homoscedasticity (P > 0.05). All our models included colony as a random
factor and an overall likelihood ratio test against an intercept only
model. In case of multiple testing, a Holm-Bonferroni correction was
performed with the adjusted P-values given throughout the text. To test
for signiﬁcant differences in the survival curves, we conducted mixed
effect cox proportional hazards regression models (Supplementary
Fig. 2) using the R package survminer (v0.4.9) followed by post-hoc
analyses using least square means with the R package lsmeans (v.2.30;
Supplementary Tables 3, 4 and 6). The survival curves were illustrated
using Kaplan-Meier cumulative survival curves (Figs. 1c, 2a, and 4, and
Supplementary Figs. 3 and 5). For the bacterial load (ΔCq) within the
thorax content between treated and untreated infected individuals
(Figs. 1a and 2b) we conducted linear mixed effect models with a least
square means (lsmeans) post-hoc analysis. For the bacterial growth
inhibition (Fig. 3d) we conducted a Mann–Whitney U test. Differences in
the CHC-proﬁle composition were calculated using a permutational
multivariate analysis of variance (ADONIS) on a Bray-Curtis dissimilarity
matrix using the package vegan (v.2.5–7; Supplementary Table 7). For
the different durations of MG care between sterile and infected individuals (Supplementary Fig. 4), we conducted a linear mixed effect model
with individual as random factor followed by a Satterthwaite’s t-test. For
differences between chemical compound groups of the CHC proﬁles
(Supplementary Fig. 6), we conducted an analysis of variance (AOV)
followed by a Tukey Honest Signiﬁcant differences test (Supplementary
Table 9). For behavioral differences in wound care between sterile and
infected individuals (Fig. 3b, c), we modeled wound care as a binary
event using binomial generalized additive models with posthoc contrasts to identify intervals of time during which the probability of
receiving wound care differed between sterile and infected individuals.
See Supplementary Information for a reproducible modeling workﬂow.

Ethics and inclusion statement
Our study brings together authors from a number of different countries but failed to include scientists based in the country where the
study was carried out. We recognize that more could have been done
to engage local scientists with our research as our project developed,
and to embed our research within the national context and research
priorities. Whenever possible, our research was discussed with local
stakeholders to seek feedback on the questions to be tackled and the
approach to be considered. Whenever relevant, literature published by
scientists from the region was also cited; efforts were made to consider
relevant work published in the local language.

Nature Communications | (2023)14:8446

https://doi.org/10.1038/s41467-023-43885-w

Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability
The raw amplicon-sequence data generated in this study have been
deposited at the Sequence Read Archive (SRA) under accession code
PRJNA826317. The sequence reads data generated in this study have
been deposited in NCBI Sequence Read Archive (SRA) under accession
code PRJNA823913. The proteomics data generated in this study have
been deposited at the ProteomeXchange Consortium via the PRIDE
partner repository under accession code PXD033003. The CHC and
MG data generated via GC-MS in this study have been deposited at the
Dryad repository under the https://doi.org/10.5061/dryad.hqbzkh1j6
[https://datadryad.org/stash/share/gFLLhPMhmWW8HJ_JaKyTqg9Eq3
QXqGS_EgxYEQvKKe8]60. The Genome Assembly data are available
under restricted access due to it being part of another Publication
within the GAGA Project47, access can be obtained by contacting the
corresponding Author of the GAGA Project. Source data are provided
as a Source Data ﬁle. Source data are provided with this paper.

Code availability
The R-code used in this study are provided in Supplementary data 1
and 2.

References
1.

2.
3.
4.
5.
6.

7.

8.
9.

10.

11.

12.

13.
14.

15.

Hart, B. L. Behavioural defences in animals against pathogens and
parasites: parallels with the pillars of medicine in humans. Philos.
Trans. R. Soc. B 366, 3406–3417 (2011).
Frank, E. T. et al. Saving the injured: rescue behavior in the termite
hunting ant Megaponera analis. Sci. Adv. 3, e1602187 (2017).
Cremer, S., Armitage, S. A. O. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, 693–702 (2007).
Stockmaier, S. et al. Infectious diseases and social distancing in
nature. Science 371, 1007–100 (2021).
Kessler, S. E. Why care: complex evolutionary history of human
healthcare networks. Front. Psychol. 11, 199 (2020).
Mascaro, A., Southern, L. M., Deschner, T. & Pika, S. Application of
insects to wounds of self and others by chimpanzees in the wild.
Curr. Biol. 32, 112–113 (2022).
Frank, E. T., Wehrhahn, M. & Linsenmair, K. E. Wound treatment and
selective help in a termite-hunting ant. Proc. R. Soc. B 285,
20172457 (2018).
Heinze, J. & Walter, B. Moribund ants leave their nests to die in social
isolation. Curr. Biol. 20, 249–252 (2010).
Pull, C. D. et al. Destructive disinfection of infected brood
prevents systemic disease spread in ant colonies. Elife 7,
e32073 (2018).
Cremer, S., Pull, C. D. & Furst, M. A. Social immunity: emergence
and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).
Frank, E. T. & Linsenmair, K. E. Individual versus collective decision
making: optimal foraging in the group-hunting termite specialist
Megaponera analis. Anim. Behav. 130, 27–35 (2017).
Diggle, S. P. & Whiteley, M. Microbe Proﬁle: Pseudomonas aeruginosa: opportunistic pathogen and lab rat. Microbiology 166,
30–33 (2020).
Yek, S. H. & Mueller, U. G. The metapleural gland of ants. Biol. Rev.
86, 774–791 (2011).
Yek, S. H., Nash, D. R., Jensen, A. B. & Boomsma, J. J. Regulation and
speciﬁcity of antifungal metapleural gland secretion in leaf-cutting
ants. Proc. R. Soc. B 279, 4215–4222 (2012).
Fernandez-Marin, H., Zimmerman, J. K., Rehner, S. A. & Wcislo, W. T.
Active use of the metapleural glands by ants in controlling fungal
infection. Proc. R. Soc. B 273, 1689–1695 (2006).
11

Article
16. Tranter, C., Fernandez-Marin, H. & Hughes, W. O. H. Quality and
quantity: transitions in antimicrobial gland use for parasite defense.
Ecol. Evol. 5, 857–868 (2015).
17. Fernandez-Marin, H. et al. Functional role of phenylacetic acid from
metapleural gland secretions in controlling fungal pathogens in
evolutionarily derived leaf-cutting ants. Proc. R. Soc. B 282,
20150212 (2015).
18. Leonhardt, S. D., Menzel, F., Nehring, V. & Schmitt, T. Ecology and
evolution of communication in social insects. Cell 164,
1277–1287 (2016).
19. Drijfhout, F., Kather, R. & Martin, S. Behavioral and Chemical Ecology
(eds W. Zhan, H. Liu) (Nova Science Pub Inc, 2009), chap. 3.
20. Holze, H., Schrader, L. & Buellesbach, J. Advances in deciphering
the genetic basis of insect cuticular hydrocarbon biosynthesis and
variation. Heredity 126, 219–234 (2021).
21. Poole, K. Efﬂux-mediated multiresistance in Gram-negative bacteria. Clin. Microbiol. Infect. 10, 12–26 (2004).
22. Hart, B. L. & Powell, K. L. Antibacterial properties of saliva - role in
maternal periparturient grooming and in licking wounds. Physiol.
Behav. 48, 383–386 (1990).
23. Primon-Barros, M. & Macedo, A. J. Animal venom peptides: potential for new antimicrobial agents. Curr. Top. Med. Chem. 17,
1119–1156 (2017).
24. Janusz, G. et al. Laccase properties, physiological functions, and
evolution. Int. J. Mol. Sci. 21, 966 (2020).
25. Yang, W. J. et al. Single amino acid substitution in homogentisate
dioxygenase affects melanin production in bacillus thuringiensis.
Front. Microbiol. 9, 2242 (2018).
26. Moretta, A. et al. Antimicrobial peptides: a new hope in biomedical
and pharmaceutical ﬁelds. Front. Cell Infect. Microbiol. 11,
668632 (2021).
27. Cushnie, T. P. T., Cushnie, B. & Lamb, A. J. Alkaloids: an overview of
their antibacterial, antibiotic-enhancing and antivirulence activities.
Int. J. Antimicrob. Agents 44, 377–386 (2014).
28. Vila, T., Rizk, A. M., Sultan, A. S. & Jabra-Rizk, M. A. The power of
saliva: antimicrobial and beyond. PLoS Pathog. 15, e1008058 (2019).
29. Leaper, D. J. Prophylactic and therapeutic role of antibiotics in
wound care. Am. J. Surg. 167, 15–20 (1994).
30. Bobrov, A. G. et al. Evaluation of Pseudomonas aeruginosa pathogenesis and therapeutics in military-relevant animal infection
models. APMIS 130, 436–457 (2022).
31. Konaté, S. & Kampmann, D. Biodiversity atlas of West Africa, 3: Côte
d’Ivoire. (Abidjan & Frankfurt am Main, 2010).
32. Schmidt, C. A. & Shattuck, S. O. The higher classiﬁcation of the ant
subfamily ponerinae (hymenoptera: formicidae), with a review of
ponerine ecology and behavior. Zootaxa 3817, 1–+ (2014).
33. Villet, M. H. Division of labor in the matabele ant Megaponera foetens (Fabr) (Hymenoptera-Formicidae). Ethol. Ecol. Evol. 2,
397–417 (1990).
34. Looke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA
from yeasts for PCR-based applications. Biotechniques 50,
325–328 (2017).
35. Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer
(ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl
Acad. Sci. USA 109, 6241–6246 (2012).
36. Boratyn, G. M., Thierry-Mieg, J., Thierry-Mieg, D., Busby, B. & Madden, T. L. Magic-BLAST, an accurate RNA-seq aligner for long and
short reads. BMC Bioinform. 20, 405 (2019).
37. Kesnerova, L. et al. Disentangling metabolic functions of bacteria in
the honey bee gut. PLoS Biol. 15, e2003467 (2017).
38. Gallup, J. M. PCR Troubleshooting And Optimization: The Essential
Guide. (eds. S. Kennedy, N. Oswald) Ch. 2 (Caister Academic
Press, 2011).
39. Kesnerova, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).

Nature Communications | (2023)14:8446

https://doi.org/10.1038/s41467-023-43885-w
40. Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal,
encapsulated proteomic-sample processing applied to copynumber estimation in eukaryotic cells. Nat. Methods 11,
300–319 (2014).
41. Cox, J. & Mann, M. MaxQuant enables high peptide identiﬁcation
rates, individualized p.p.b.-range mass accuracies and proteomewide protein quantiﬁcation. Nat. Biotechnol. 26, 1367–1372 (2008).
42. Cox, J. et al. Andromeda: a peptide search engine integrated into
the maxquant environment. J. Proteome Res. 10, 1794–1805 (2011).
43. Schwanhausser, B. et al. Global quantiﬁcation of mammalian gene
expression control. Nature 473, 337–342 (2011).
44. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13,
731–740 (2016).
45. Naamati, G., Askenazi, M. & Linial, M. ClanTox: a classiﬁer of short
animal toxins. Nucleic Acids Res. 37, W363–W368 (2009).
46. Steinegger, M. & Soding, J. MMseqs2 enables sensitive protein
sequence searching for the analysis of massive data sets. Nat.
Biotechnol. 35, 1026–1028 (2017).
47. Boomsma, J. J. et al. The global ant genomics alliance (GAGA).
Myrmecol. News 25, 61–66 (2017).
48. Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA
117, 9451–9457 (2020).
49. Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native
and syntenically mapped cDNA alignments to improve de novo
gene ﬁnding. J. Bioinform. 24, 637–644 (2008).
50. Lomsadze, A., Ter-Hovhannisyan, V., Chernoff, Y. O. & Borodovsky,
M. Gene identiﬁcation in novel eukaryotic genomes by self-training
algorithm. Nucleic Acids Res. 33, 6494–6506 (2005).
51. Hoff, K. J., Lange, S., Lomsadze, A., Borodovsky, M. & Stanke, M.
BRAKER1: unsupervised RNA-Seq-Based genome annotation with
geneMark-ET and AUGUSTUS. J. Bioinform. 32, 767–769 (2016).
52. Bruna, T., Hoff, K. J., Lomsadze, A., Stanke, M. & Borodovsky, M.
BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR
Genom. J. Bioinform 3, lqaa108 (2021).
53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
54. Drost, H. G., Gabel, A., Grosse, I. & Quint, M. Evidence for active
maintenance of phylotranscriptomic hourglass patterns in animal
and plant embryogenesis. Mol. Biol. Evol. 32, 1221–1231 (2015).
55. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic
local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
56. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efﬁcient general
purpose program for assigning sequence reads to genomic features. J. Bioinform. 30, 923–930 (2014).
57. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold
change and dispersion for RNA-seq data with DESeq2. Genome Biol.
15, 550 (2014).
58. R: A Language And Environment For Statistical Computing (R
Foundation for Statistical Computing, Vienna, Austria, 2013).
59. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (SpringerVerlag New York, 2009).
60. Frank, E., Keller, L. Targeted treatment of injured nestmates with
antimicrobial compounds in an ant society [Dataset]. Dryad https://
doi.org/10.5061/dryad.hqbzkh1j6 (2023).

Acknowledgements
We thank the Comoé National Park Research Station for the use of their
facilities for the ﬁeld and laboratory research and the park management
of Ofﬁce Ivoirien des Parcs et Réserves for facilitating ﬁeld research in
the park. We thank GAGA for providing the assembled Megaponera
analis genome. We thank Barbara Milutinovic and the Cremer lab at IST
Austria for teaching us the methodology for the antimicrobial assay.

12

Article
We thank David Kouassi and Abou Ouattara for their help with nest
excavation and colony maintenance in the ﬁeld. We thank Christine La
Mendola for her help with sample preparation for genetic analyses and
Camille Lavoix for proof reading of the manuscript. This study was
supported by the Swiss NSF grant 310030_156732 and the ERC
Advanced Grant resiliANT (no: 741491) to LaK. ACL was supported by
Swiss NSF grant PR00P3_179776. ETF was supported by the DFG Emmy
Noether Programme (no: 511474012).

https://doi.org/10.1038/s41467-023-43885-w
Correspondence and requests for materials should be addressed to
Erik. T. Frank or Laurent Keller.
Peer review information Nature Communications thanks Axel Touchard,
and the other, anonymous, reviewer(s) for their contribution to the peer
review of this work. A peer review ﬁle is available.
Reprints and permissions information is available at
http://www.nature.com/reprints

Author contributions
Conceptualization: E.T.F., La.K. Methodology: E.T.F., Lu.K., J.L., Q.H.,
A.C.L., A.D., F.A., E.P.E., P.W., T.S., and D.B.S. Investigation: E.T.F., Lu.K.,
J.L., Q.H., A.C.L., E.P.E., and T.S. Visualization: E.T.F., Q.H., F.A., E.P.E.,
A.C.L., J.L., and D.B.S. Funding acquisition: La.K. and E.T.F. Project
administration: E.T.F., P.E., and La.K. Supervision: E.T.F. and La.K. Writing
– original draft: E.T.F. and La.K. Writing – review & editing: E.T.F., Lu.K.,
J.L., Q.H., A.C.L., A.D., F.A., E.P.E., P.W., P.E., T.S., D.B.S., and La.K.

Funding
Open Access funding enabled and organized by Projekt DEAL.

Competing interests
The authors declare no competing interests.

Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-023-43885-w.

Nature Communications | (2023)14:8446

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if
changes were made. The images or other third party material in this
article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons license and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
© The Author(s) 2023

13

