# The major role of the REL2/NF-κB pathway in the regulation of midgut bacterial homeostasis in the malaria vector Anopheles gambiae

Suzana Zaković, Galo E Rivera, Remmora Gomaid, Cristina Graham

Martinez, Christine Kappler, Eric Marois, Elena A Levashina

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## ► To cite this version:

Suzana Zaković, Galo E Rivera, Remmora Gomaid, Cristina Graham Martinez, Christine Kappler, et al.. The major role of the REL2/NF-κB pathway in the regulation of midgut bacterial homeostasis in the malaria vector Anopheles gambiae. Cell Reports, 2025, 44, 10.1016/j.celrep.2025.116282. hal-05290752

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Submitted on 30 Sep 2025

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**Cell Reports**

# The major role of the REL2/NF-κB pathway in the regulation of midgut bacterial homeostasis in the malaria vector *Anopheles gambiae*

## Graphical abstract

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## Authors

Suzana Zaković, Galo E. Rivera, Remmora Gomaid, Cristina Graham Martinez, Christine Kappler, Eric Marois, Elena A. Levashina

## Correspondence

levashina@mpiib-berlin.mpg.de

## In brief

Zakovic et al. show that the immune regulator REL2 maintains *Anopheles gambiae* midgut homeostasis by activating antimicrobial peptide genes after blood feeding. REL2 loss causes *Serratia*-driven dysbiosis and mosquito mortality. *Serratia* overgrowth in REL2-deficient mosquitoes inhibits *Plasmodium falciparum* colonization, highlighting a microbiota-mediated barrier to malaria transmission.

## Highlights

* REL2 regulates antimicrobial peptide expression in the *A. gambiae* midgut after blood meal

* Disruption of midgut immunity leads to bacterial dysbiosis and increases mosquito mortality

* Dysbiosis and mortality in REL2-deficient mosquitoes associate with *Serratia* overgrowth

* *Serratia* overgrowth in the midgut inhibits *Plasmodium falciparum* midgut colonization

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Article

# The major role of the REL2/NF-κB pathway in the regulation of midgut bacterial homeostasis in the malaria vector *Anopheles gambiae*

Suzana Zaković,<fnref n="1" /> Galo E. Rivera,<fnref n="1" /> Remmora Gomaid,<fnref n="1" /> Cristina Graham Martinez,<fnref n="1" />,<fnref n="3" /> Christine Kappler,<fnref n="2" /> Eric Marois,<fnref n="2" /> and Elena A. Levashina<fnref n="1" />,<fnref n="4" />,*

## SUMMARY

Multicellular organisms harbor diverse microbial communities that play essential roles in host physiology. While often beneficial, these interactions require tight regulation to prevent dysbiosis and disease. This study examines the tissue-specific immune responses of mosquitoes to blood feeding and *Plasmodium falciparum* infection in *Anopheles* females. We demonstrate that REL2 regulates the expression of antimicrobial peptide genes and shapes midgut bacterial composition post-blood meal. Loss of REL2 leads to midgut dysbiosis, characterized by the overgrowth of *Serratia* spp., and mosquito lethality after blood feeding. Interestingly, *Serratia*-induced dysbiosis also reduces *P. falciparum* prevalence in surviving mosquitoes. Our findings highlight the critical role of the immune system in maintaining midgut bacterial homeostasis and uncover complex interactions between mosquito immunity, gut microbiota, and malaria parasites.

## INTRODUCTION

All multicellular organisms are inhabited by microbes that critically contribute to their physiology. While most microbes are harmless or beneficial to their hosts, such homeostatic interactions must be tightly controlled as even small perturbations in the system could lead to dysbiosis and disease.

The immune system plays a crucial role in maintaining homeostatic interactions with microbial communities at epithelial surfaces in mammals. In insects, most research has focused on the microbiomes of the digestive tract. While beetles and bees harbor stable, well-defined microbial communities within distinct gut compartments,<fnref n="1" />,<fnref n="2" /> the mosquito midgut features a more diverse and dynamic microbiome.<fnref n="3" />,<fnref n="4" /> Disruptions to these characteristic microbial communities by opportunistic environmental microorganisms can lead to pathogenic situations. However, how the immune system responds to nutritional stressors and other perturbations in gut microbial communities remains poorly understood. Studies in *Drosophila melanogaster* have been instrumental in elucidating insect antibacterial responses, revealing that a healthy immune response requires a balance between microbial sensing, signal transduction, and effector function. Recognition of conserved microbial structures by a diverse array of molecules triggers both humoral and cellular immune responses, shaping interactions between the host and its microbiota.⁵

*Drosophila*'s humoral immunity is characterized by the induction of small cationic peptides with potent antimicrobial activity,

collectively called antimicrobial peptides (AMPs). Microbial sensing by peptidoglycan recognition receptors (PGRPs) activates immune signaling pathways that induce AMP expression. Cellular immune responses, such as phagocytosis and encapsulation, are triggered through scavenger receptors, thioester-containing proteins (TEPs) and lectins. The recognition of microbial molecules can also trigger a series of extracellular proteolytic cascades leading to melanization, or deposition of melanin at the site of infection or injury, which is mediated by both cellular components (hemocytes) and humoral factors, including CLIP-domain serine proteases and prophenoloxidases.

The Toll and immune-deficiency (IMD) nuclear factor (NF)-κB-like pathways are key signaling modules in *Drosophila* that mediate immune responses triggered by bacteria and fungi.⁶ The Toll pathway is activated mainly by I-lysine-type (Lys-type) peptidoglycans (PGNs) of Gram-positive bacteria and by β-glucans of fungi through binding to circulating receptors (peptidoglycan-recognition protein - shortA [PGRP-SA]) and Gram-negative bacteria-binding proteins 1 and 3, leading to activation of the transcription factor Dif and expression of AMPs such as drosomycins, metchnikowin, and bomanins.⁷⁻⁹ The IMD pathway is induced by sensing of meso-diaminopimelic acid-type (Dap-type) PGNs of Gram-negative bacteria by the membrane-bound PGRP-long C (PGRP-LC) and intracellular PGRP-long E (PGRP-LE) receptors, culminating in the activation of transcription factor Relish and expression of attacin, cecropin, defensin, diptericin, and drosocin. IMD activity in the fruit fly's midgut is essential for control of pathogenic bacteria and

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<footnote n="1">¹Vector Biology Unit, Max-Planck Institute for Infection Biology, 10115 Berlin, Germany</footnote>

<footnote n="2">²CNRS UPR9022, INSERM U1257, Université de Strasbourg, 67084 Strasbourg, France</footnote>

<footnote n="3">³Present address: IGMP, University of Bern, 3012 Bern, Switzerland</footnote>

<footnote n="4">⁴Lead contact</footnote>

<footnote>*Correspondence: levashina@mpiib-berlin.mpg.de</footnote>

<footnote>https://doi.org/10.1016/j.celrep.2025.116282</footnote>
maintenance of a healthy microbiome.<sup>10–14</sup> In systemic infections, when bacteria are introduced into body cavity by injections and spread through hemolymph, the main responding tissues are the fat body, analogue of the mammalian liver, and the blood cells, also called hemocytes.<sup>15,16</sup> However, the midgut functions of the IMD pathway and AMPs in physiological conditions are less well understood.

The lifestyle and microbial exposure of *Anopheles gambiae* mosquitoes, a major vector of the human malaria parasite *Plasmodium falciparum* (PF) in Africa, differ significantly from those of *Drosophila*. Adult *Anopheles* mosquitoes acquire microbes through feeding on water and plant nectar. In addition to carbohydrate-rich nectar, females require a mammalian blood meal, which provides essential proteins and lipids for ovarian development. This nutrient-rich diet also fuels rapid proliferation of midgut bacteria, peaking one day after feeding.<sup>17–20</sup> These physiological and immune challenges trigger extensive transcriptional changes and tissue remodeling in the gut, including the synthesis of a protective chitinous glycoprotein-rich structure called the peritrophic matrix.<sup>21,22</sup> The peritrophic matrix forms a semi-permeable mash around the blood bolus during meal digestion and may protect the midgut cells from microbial invasion. Blood feeding (BF) also exposes female mosquitoes to *Plasmodium* infection. Once in the midgut lumen, malaria parasites undergo sexual reproduction, producing ookinetes—the motile stage that invades the midgut epithelium. Ookinete development coincides with the peak of bacteria proliferation in the posterior midgut. Successful ookinetes traverse the epithelium and convert into oocysts on the basal side of the midgut, where parasite development continues until the formation of sporozoites.

The regulation of immune responses in *Anopheles* mosquitoes differs from that in *Drosophila*.<sup>23</sup> For example, mosquitoes lack the intracellular receptor PGRP-LE, while PGRP-LC has been shown to bind PGNs from both Gram-positive and Gram-negative bacteria.<sup>24</sup> Additionally, mosquitoes do not produce diptericinics, drosocins, or any AMPs regulated by the Toll pathway. Instead, their major AMPs include the conserved defensins (DEF1–5), cecropins (CEC1–4), attacin, and the mosquito-specific gambicin (GAMB).<sup>25</sup>

The highest expression levels of AMP transcripts (CEC1–4, DEF1, and GAMB) were found in the cardia.<sup>26,27</sup> The cardia is situated at the junction of the ectoderm-derived foregut and the endoderm-derived midgut. This region likely functions as a sphincter, regulating the passage of food from the foregut into the midgut, and through the expression of AMPs, it provides a protective barrier that limits pathogen entry into the midgut, the functional analog of the mammalian stomach.

Enzymatic and transcriptomic data suggest that the anterior midgut is involved in the processing of sugars and proteins, while the posterior midgut specializes in digesting proteins and absorbing lipids, with enzymatic activities increasing after blood meal.<sup>26–28</sup> Currently, a tissue-specific gene expression atlas for the midgut tissues, including the cardia, anterior, and posterior midgut, is only available for unfed *Anopheles* mosquitoes, whereas transcriptional changes in midgut regions following a blood meal have been exclusively documented in *Aedes aegypti*.<sup>27</sup> A notable difference in the posterior midgut between

unfed *Ae. aegypti* and *A. gambiae* is the high expression levels of AMPs and the low expression levels of digestive serine endopeptidases in *A. gambiae*.<sup>27</sup> This suggests potentially distinct immune and digestive strategies between the two species. It remains unknown whether these differences persist after BF, highlighting a knowledge gap in our understanding of the post-blood meal transcriptional landscape in *Anopheles*.

The regulation of immune responses and AMP function in *Anopheles* remains only partially understood. RNA interference (RNAi) studies aimed at identifying effector genes of the Toll/REL1 and IMD/REL2 pathways suggested that both contribute to the transcriptional regulation of numerous immune genes.<sup>29,30</sup> However, an earlier *in vitro* study reported that REL2 specifically regulates expression of *CEC1*, *CEC3*, *GAMB*, and *leucine-rich-repeat immune 1 (LRIM1)* genes.<sup>31</sup> Functional studies of the REL2 pathway yielded contradictory results regarding its role in antibacterial and antiparasitic responses. For example, RNAi-mediated silencing of a negative regulator or transgenic overexpression of the REL2 transcription factor has produced varied outcomes, ranging from no detectable effect to complete mosquito resistance to *Plasmodium* infections.<sup>30,32,33</sup> Conversely, down-regulation of the REL2 pathway only mildly increased mosquito susceptibility to parasites<sup>24,34,35</sup> but significantly increased bacterial overgrowth in the midgut<sup>20,24</sup> and mosquito mortality after bacterial injection.<sup>24,31</sup> Evidence also suggested that bacteria may contribute to mosquito immune responses against parasites or directly inhibit *Plasmodium* development in the midgut. Indeed, antibiotic treatments have been shown to increase parasite loads.<sup>20,24,36,37</sup> Despite these findings, the interplay between the REL2 pathway, midgut bacteria, and *Plasmodium* infections remains poorly understood.

The challenge of integrating diverse research approaches contributes to our incomplete understanding of these interactions. Previous studies on immune pathway gene targets often used whole-organism RNAi knockdown, which can vary in tissue-specific efficiency.<sup>29,30</sup> In contrast, tissue-specific studies show that *REL2* overexpression significantly affects metabolic and regulatory genes, with minimal impact on immune genes.<sup>33,38</sup> Furthermore, antibacterial responses have been examined mostly under non-physiological conditions, such as thoracic injections or oral feeding with excessive doses of non-commensal bacteria species.

The *Anopheles* mosquito's lifestyle—characterized by repeated BFs that drive bursts of bacterial proliferation and *Plasmodium* infection—provides an ideal model for examining immune responses in a physiological context. In this study, we investigated the tissue-specific responses of *Anopheles* to BF and PF infection in both wild-type (WT) and REL2-deficient mosquitoes, focusing on key immune tissues: blood cells and fat body, the cardia with the anterior midgut, and the posterior midgut. Our findings show that REL2 plays a critical role in regulating AMP expression in the anterior and posterior midgut 24 h after BF. Moreover, microbiome sequencing suggests that REL2 shapes bacterial composition in the midgut and is essential for female survival. Notably, we demonstrate that the inhibition of mosquito colonization by PF in the midgut of *REL2* mutants is caused by proliferation of *Serratia* spp. Our results identify REL2 as a key factor in maintaining midgut
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<figure><img src="image_7.png" /><figcaption>**Figure 1. Phenotyping of the REL2<sup>-/-</sup> mutant line**</figcaption></figure>

(A and B) RT-qPCR quantification of immune gene expression in the wild-type (WT) and REL2<sup>-/-</sup> mosquitoes, normalized to the mosquito RPS7 gene. Dots show pooled samples from independent experiments (N = 3, n = 10). Crossbars indicate means of all experiments. Lines connect means of the experiments grouped by experimental condition. Statistically significant differences were evaluated using Student's t test (*p* < 0.05; **p* < 0.01). (A) Mosquitoes were injected with PBS (–) or with a bacterial mix (*E. coli* and *S. aureus*) (+), and 4 h post-injection, tissues were dissected: posterior midgut (top row), hemocytes (middle row), and abdominal carcass with fat body (bottom row). (B) Whole mosquito samples were collected before blood feeding (unfed [UF]) and 24 h after blood feeding (BF) and *P. falciparum* (PF) infection.

bacterial homeostasis and offer new insights into the complex interactions between mosquito immunity, midgut microbiota, and malaria parasites.

## RESULTS

### Establishment of REL2 mutants

To explore the role of REL2 in physiological mosquito immune responses, we used CRISPR-Cas9 technology for targeted mutation of the *REL2* locus (*REL2*<sup>-/-</sup>) by inserting a *3xP3::GFP::SV40* cassette into the sequence encoding the Rel homology domain (RHD; Figure S1).

The impact of REL2 knockout on the expression levels of potential target genes was assessed following control thoracic injection of phosphate-buffered saline (PBS) or a bacterial mixture of *Escherichia coli* and *Staphylococcus aureus*. Hemocytes, posterior midgut, and abdominal carcass (containing the fat body) were collected 4 h post-injection, and the expression of a set of known immune genes was quantified using RT-qPCR. This time point was chosen based on our earlier data

on the induction of immune gene expression following a bacterial challenge.<sup>39</sup>

Bacterial injection significantly increased the transcript levels of *CEC1*, *DEF1*, *FBN9*, and *TEP1* in hemocytes, while *CEC1* and *FBN9* were up-regulated in the carcass/fat body. Notably, compared to control, bacterial injection significantly elevated *GAMB* transcript levels in the posterior midgut but not in the other tissues tested (Figure 1A). These transcriptional changes induced by bacterial injection were absent in *REL2* mutants, confirming the loss-of-function phenotype of the CRISPR/Cas9-mediated genetic modification. Interestingly, bacterial injection decreased the expression levels of *CEC2* in the carcass of WT mosquitoes but not *REL2* mutants.

Overall, our results indicated tissue-specific expression patterns of immune gene expression. Specifically, *CEC1*, *DEF1*, and *CEC3* transcripts were detected in all three analyzed tissues; *TEP1* was found in hemocytes and the carcass; *GAMB* and *CEC2* were primarily observed in the posterior midgut; while *FBN9* was detected predominantly in hemocytes (Figure 1A).

We then evaluated the impact of REL2 deficiency on the expression of the selected genes following BF and PF infection.
A

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B

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C

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D

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E

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F

<figure><img src="image_14.png" /><figcaption>**Figure 2.** Regulation of immune gene expression by the REL2 pathway in *A. gambiae* midgut</figcaption></figure>

(A) Schematic representation of mosquito tissues used in transcriptomic analyses.

(B) UMAP plots of RNA-seq data with identified clusters (circled) colored by tissue (first panel), feeding condition (middle panel) - unfed (UF; blue), blood fed (BF; yellow), *Plasmodium falciparum* (PF) infected (black), and mosquito strain (third panel). Each dot represents one sample (N = 3).

(C) PCA of RNA-seq data for each tissue for 500 most differentially expressed genes. Samples are grouped by strain and feeding condition.

(D) Treemaps depicting the top 50% of the transcriptome (in transcripts per million [TPM]), grouped and colored by functional category. Each square with a white outline represents the expression proportion of an individual gene. Only gene categories discussed in the text are labeled with color codes.

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Extracts from whole WT or REL2⁻/⁻ mosquitoes were collected before (unfed) and 24 h after either BF or PF infection. While BF and PF did not significantly affect gene expression, REL2 deficiency decreased the expression levels of CEC1, GAMB, DEF1, and CEC3 in all conditions. A modest reduction in CEC2 expression was observed in unfed REL2⁻/⁻ compared to WT mosquitoes (Figure 1B). These results confirmed the loss-of-function phenotype of the REL2 mutants. Considering the tissue-specific expression of immune genes, further transcriptional analyses were performed using tissue-specific RNA sequencing to obtain a broader picture of the role of REL2 in mosquito responses to BF and PF infection.

## Identification of the REL2-regulated tissue-specific processes by transcriptome sequencing

To understand the role of REL2 in mosquito responses to BF and PF infection, we examined WT and REL2⁻/⁻ mosquitoes before BF and 24 h after feeding with uninfected blood or blood infected with PF. Our analysis focused on four key immune tissues: hemocytes, fat body, the cardia with the anterior midgut, and the posterior midgut, which is the site of *Plasmodium ookinete* invasion (Figure 2A).

Uniform manifold approximation and projection (UMAP) analysis of the entire dataset identified four distinct clusters of mosquito samples, each corresponding to a specific tissue type (Figure 2B, left panel). However, a few mismatched samples were observed: two anterior midgut samples grouped with hemocytes, and one fat body sample clustered with the posterior midgut group. Analyses of the posterior midgut and fat body transcriptomes revealed a distinct separation between unfed samples (Figure 2B, middle, blue points) and BF samples, regardless of infection status (Figure 2B middle, black and yellow points). Surprisingly, differential clustering between WT and REL2⁻/⁻ strains was detected only for the cardia+anterior midgut and posterior midgut samples (Figure 2B, right panel), highlighting tissue-specific effects of REL2 deficiency.

To confirm tissue-specific clustering, we selected 20 highly expressed genes in WT mosquitoes (UMAP, $p_{adj} < 0.05$) and plotted their expression patterns across all tissues (Figure S2A). Expression of most of these genes matched the previously reported tissue-specific patterns, validating the tissue specificity of our transcriptome analyses. The hemocyte transcriptome was enriched with genes encoding prophenoloxidases (PPOs) and CLIP-serine proteases, which are involved in melanization processes, and with fibrinogen-like proteins (FBNs).⁴⁰ The fat body transcriptome was primarily characterized by genes encoding leucine-rich repeat proteins and those involved in lipid metabolism. Consistent with previous reports,²⁷ the transcriptome of the cardia and anterior midgut was dominated by genes encoding AMPs, the putative transcriptional activator multiprotein bridge

factor 2 (MBF2), the peritrophic matrix (PM) protein Tsetse EP, and carbohydrate digestive enzymes (Figures S2A and S2B). In the posterior midgut, genes coding for inducible digestive trypsin and carboxypeptidases were highly enriched, particularly after BF (Figures S2A and S2C). As previously reported, expression levels of serine protease genes in the unfed *A. gambiae* posterior midgut were approximately 2.5-fold lower than those in *Ae. aegypti*.²⁷ However, BF induced a significant increase in serine protease expression levels that became comparable to those observed in *Aedes*.

Next, we used principal component analysis (PCA) to investigate the effect of BF on tissue-specific gene expression (Figure 2C). No differences were observed in hemocyte samples under any condition. However, a clear separation was detected between unfed and BF (infected or not) samples in the fat body, cardia+anterior midgut, and posterior midgut (Figure 2C). Additionally, PCA revealed significant transcriptional differences between WT and REL2⁻/⁻ mosquitoes in the cardia+anterior and posterior midgut. In contrast, no notable differences were detected in the fat body and hemocyte samples. These results suggested that REL2 regulates midgut-associated responses after BF.

To connect the observed transcriptional changes with potential biological functions, we performed Gene Ontology (GO) term analyses on differentially expressed genes (DEGs) detected after BF in WT mosquitoes. In the fat body and posterior midgut of unfed mosquitoes, GO analyses revealed enrichment in processes such as RNA processing, ribosome biogenesis, and related metabolic and catalytic activities (Figure S2D). In particular, protein and lipid transport and glycosylation were enriched in the fat body; lipid transport, proteolysis, and peptidase activity in the cardia+anterior midgut; and fatty acid beta-oxidation, iron ion homeostasis, and intracellular cholesterol transport in the posterior midgut (Figure S2D). The fat body showed the strongest response to BF, with 492 up-regulated genes. In the posterior midgut, we identified 231 up-regulated genes, while 136 genes were up-regulated in the cardia+anterior midgut. The processes that were primarily induced by BF in these tissues were related to metabolism and digestion rather than immunity.

To explore the role of REL2 in biological processes within midgut regions, we analyzed transcriptional differences between WT and REL2⁻/⁻ mosquitoes. We adapted a data visualization technique from a recent study, where tissue-specific transcriptional investment was assessed by examining the relative abundance of the top 20 genes expressed as transcript read counts per million (TPM) and categorized by function.²⁷ Our analyses focused on the top 50% of the transcriptome for each sample type (tissue, mosquito strain, and feeding condition). Significant differences in transcriptional complexity were observed in WT between the cardia+anterior and the posterior midgut. In the cardia and anterior midgut, only 10 genes accounted for the 50% of

<footnote>(E) Expression of REL2-regulated immune genes in the cardia+anterior and posterior midgut, identified by DESeq2 ($p_{adj} < 0.05$). Statistically significant differences were evaluated using the Student's t test (*p* < 0.05; **p* < 0.01; ***p* < 0.001; ****p* < 0.0001). UF: unfed; BF: blood-fed; PF: *P. falciparum*. Dots represent pooled samples of independent experiments (N = 3, n = 10 hemocytes, 5 other tissues). Crossbars represent means of all experiments. Lines connect means of experiments grouped by experimental condition.</footnote>

<footnote>(F) Fluorescence microscopy images of midguts from unfed mosquitoes carrying the *DEF1::GFP* reporter in either heterozygous (*DEF1-GFP;REL2⁺/⁺*, left panel) or homozygous (*DEF1-GFP;REL2⁻/⁻*, right panel) *REL2* mutants. The arrows show the transition between the cardia and anterior midgut, and the dashed line marks the cardia-proximal region with REL2-dependent *DEF1* expression.</footnote>
the total transcriptome. Among these, 27% encoded AMPs, 10% encoded PM proteins, 10% encoded the unknown protein AGAP013543, and 3% encoded a potential transcriptional factor MBF2 (Figure 2D). REL2 knockout reduced by 2-fold levels of AMP gene expression (Figure 2D left panel), while the expression levels of other functional categories, including PM proteins, remained unchanged. Although REL2 deficiency did not significantly alter transcriptional investment after BF, it caused a 2-fold increase in the expression of genes encoding serine proteases (SP) at the expense of AGAP013543 (Figure 2D).

In contrast to the highly specialized expression profile of the
cardia+anterior midgut, 50% of the posterior midgut transcrip-
tome included 126 genes encoding protein families such as ribo-
somal proteins, digestive serine proteases, cytochrome oxidases
(COXs), and PM proteins. BF induced a significant shift in the
transcriptional repertoire, favoring the enrichment of transcripts
encoding digestive serine proteases (SPs; 38%) and the protein
of unknown function G12 (4%; Figure 2D). This shift was accom-
panied by a reduction in the expressional investment into ribo-
somal proteins, rRNA, PM proteins, and COXs. The impact of
BF on the transcription of digestive enzyme genes in the posterior
midgut was evident across the entire transcriptome (Figure S2C).
Similar changes were observed in REL2 mutants, with the excep-
tion of G12, whose expression was 2-fold higher than in WT
mosquitoes. Notably, the investment in AMPs observed in unfed
WT mosquitoes was lost following BF and in REL2 mutants. These
findings suggest that REL2 regulates AMP gene expression in
both regions of the midgut (Figure 2D).

We next examined REL2-mediated processes in the midgut tissues by analyzing DEGs between the transcriptomes of WT and *REL2* mutant samples. Out of 75 DEGs identified in the cardia+anterior midgut of *REL2* mutants, 60 were down-regulated and 15 up-regulated. Posterior midgut in *REL2* mutants featured 133 DEGs (81 down-regulated and 52 up-regulated). GO term analysis confirmed that REL2 deficiency resulted in significant suppression of immune- and antibacterial defense processes in both midgut sections (Figure S2E). We also observed modest changes in metabolic processes in the posterior midgut and detoxification processes in the anterior midgut.

The GO term category most profoundly affected by REL2 defi-
ciency was the expression of AMP-encoding genes, including
GAMB, CEC1, two C-type lysozymes (LYSC1 and LYSC5), and
DEF1 (Figures 2E and S3A). This category was accompanied
by altered expression levels of genes encoding C-type lectins
(CTLMA2 and CTL4), PGRP receptors (short PGRP-S1, -S2,
-S3, and long PGRP-LB), FBNs (FREP11 and FBN25), and the
serine-protease inhibitor 9 (SRPN9). While the cardia+anterior
midgut exhibited the highest AMP expression levels among all
sequenced tissues (Figures S2A, S2B, and S3A), the posterior
midgut also showed strong immune gene expression, including
GAMB, LYSC1, CTLMA2, CTL4, PGRP-S2, -S3, FREP11, and
FBN25 (Figures 2E and S2B).

Our analysis confirmed high levels of expression of immune
genes in the cardia+anterior midgut, regardless of the feeding
condition. In the posterior midgut, BF strongly induced the
expression of PGRP-S2, -S3, LRIM4, and TEP3, while the
expression levels of GAMB, LYSC1, PGRP-LB, CTLMA2, and
CTL4 were only mildly up-regulated (Figures 2E and S2F). Lower

levels of FBN25 and FREP11 expression after BF in WT mosqui-
toes were further decreased in REL2 mutants. Interestingly, the
expression of LYSC5, PGRP-S1, FBN50 (Figure 2E), CEC3,
SRPN9, and FREP12 (Figure S2F) was specifically regulated by
REL2 in the cardia and anterior midgut. Conversely, REL2-medi-
ated induction of LRIM4 and TEP3 after BF was only detected in
the posterior midgut (Figure S2F).

Unexpectedly, the expression of PGRP-LC in the cardia+ante-
rior midgut, as well as its blood-induced upregulation in the
posterior midgut, was independent of REL2 (Figure S3B). While
we observed higher levels of REL2 transcripts in REL2 -/- mu-
tants (Figure S3B), most of the sequencing reads were mapped
to truncated non-functional REL2 transcripts (Figure S3D).

Previous reports described the constitutive expression of AMP
genes in the cardia.²⁶,²⁷,⁴¹ To visualize the expression of the REL2
target gene *defensin 1* in the midgut *in vivo*, we used a mosquito
transgenic reporter line that expresses *GFP* under the *DEF1* pro-
moter⁴² and crossed this line with a *REL2*-deficient homozygous
REL2<sup>-/-</sup> line to create a heterozygous (*DEF1::GFP*; *REL2<sup>+/−</sup>* ) and
homozygous (*DEF1::GFP*; *REL2<sup>-/-</sup>* ) mosquitoes. In the midguts
of *REL2* heterozygous mosquitoes, the strongest GFP signal
was observed in the cardia and the adjacent part of the anterior
midgut (Figure 2F, left panel). In contrast, *DEF1* reporter expres-
sion was significantly reduced in *REL2<sup>-/-</sup>* mosquitoes, with GFP
fluorescence primarily detected in the cardia (Figure 2F, right
panel). These results support our transcriptional analyses and
demonstrate that REL2 regulates *DEF1* expression in the car-
dia-proximal region of the anterior midgut, while remaining levels
of *DEF1* expression in the cardia are REL2 independent.

Finally, we examined the transcriptional responses mediated
by REL2 at 24 h after PF infection and identified eight DEGs
in the posterior midgut of REL2 mutants compared to WT
(Figure S3C). Among these, two genes were significantly
down-regulated in REL2 mutants after BF and PF infection:
*Mucin-2*<sup>43</sup> and a gene encoding a PM-associated protein.<sup>44</sup>
Interestingly, PF infection of REL2 mutants, but not BF, modestly
yet statistically significantly induced the expression of these
genes and of three genes involved in intracellular transport pro-
cesses (Rab-8A, Rhomboid4B, and nucleolar complex protein3).
Conversely, transcript levels of carbonic anhydrase, syntaxin 16,
and scavenger receptor class A in REL2 mutants were modestly
down-regulated by parasite infection. These results suggest that
PF midgut colonization does not trigger strong REL2-mediated
transcriptional responses at 24 h after infection. However, the
role of REL2 in regulating certain components of the PM, vesic-
ular transport, and proteins potentially involved in tissue stress
responses warrants further examination.

We concluded that the REL2-mediated transcriptional regula-
tion of both the upstream immune sensing module and down-
stream effector AMPs in the midgut, before and after BF, offers
a robust model for investigating the functional consequences
of REL2 depletion on mosquito survival and susceptibility to PF.

REL2 depletion promotes midgut bacteria growth and
mosquito lethality

WT mosquitoes and REL2 mutants were infected with PF, and
11 days later, the percentage of infected mosquitoes (infection
prevalence) and the number of oocysts (infection load)
<figure><img src="image_15.png" /><figcaption>Figure 3. Immune suppression causes bacterial overgrowth and increased mortality in *REL2*<sup>-/-</sup> mosquitoes, without affecting susceptibility to *P. falciparum*</figcaption></figure>

(A, C, and F) Prevalence of *P. falciparum* infection and oocyst numbers (excluding uninfected mosquitoes) for wild-type (WT; black) and *REL2*<sup>-/-</sup> (yellow) mosquitoes, 11 days after infection. Dots represent prevalence or median oocyst numbers of independent experiments; lines connect results of the same experiment. Crossbars indicate medians of all experiments. (A) *N* = 10, *n* = 18–40; (C) *P. falciparum* infection levels in survival experiments in (B) (*N* = 3, *n* = 12–30); (F) untreated (CTRL) and antibiotic-treated (AB) mosquitoes (*N* = 4, *n* = 15–30).

(B and E) Kaplan-Meier survival analysis of WT and *REL2*<sup>-/-</sup> mosquitoes after blood feeding and (B) *P. falciparum* infection (*N* = 3, *n* = 30–70); (E) with prior antibiotic treatment (AB) or without (CTRL) (*N* = 3, *n* ≥ 50). Statistically significant differences were evaluated with log rank test, ns: non-significant.

(D) Colony forming units (CFUs) from plated mosquito homogenates, isolated from unfed or blood fed mosquitoes. Dots show CFUs of individual mosquitoes (*N* = 3, *n* = 10). Boxplots indicate the median and interquartile range (IQR), and whiskers represent 1.5× IQR. (A, C, D, F) Statistically significant differences were evaluated using the Wilcoxon rank-sum test, ns, *p* > 0.05; ***p* < 0.001; ****p < 0.0001.

were quantified in the dissected midguts. High variability was observed between experimental repeats, and no significant difference between WT and *REL2* mutants was detected (Figure 3A). However, we noted high mortality rates in the *REL2*-deficient group. To test whether the death of immuno-compromised mosquitoes was induced by PF infection, we quantified female survival after BF and PF infection. We found that the increased mortality in *REL2* mutants was due to BF and not PF infection (Figure 3B), regardless of the infection outcome (Figure 3C).

The high variability in infection outcomes and the mortality of mutant mosquitoes after BF suggested that both phenotypes may result from uncontrolled bacterial proliferation in the midgut. Therefore, we compared bacterial loads in WT and *REL2* mutants by counting colony-forming units (CFUs) from plated homogenates of whole mosquitoes collected before and 24 h after BF. Significantly higher bacterial loads were observed in the *REL2* mutants compared to WT mosquitoes, both before and after BF (Figure 3D), indicating a link between immunosuppression and bacterial overgrowth. To confirm that mortality observed in *REL2* mutants was caused by bacteria, we treated WT and *REL2*-deficient females with sugar meals containing a mix of antibiotics before BF. The antibiotic treatment fully rescued mutants' survival and also improved the survival of WT mosquitoes, particularly in experiments with low survival rates in both groups (Figure 3E).

We next re-examined the role of REL2 in mosquito susceptibility to PF infection after antibiotic treatment. Antibiotic treatment reduced inter-experimental variability and slightly increased infection prevalence and oocyst load, but no statistically significant differences in PF prevalence and infection loads were detected between WT and *REL2* mutants (Figure 3F).

Overall, our findings suggested that REL2-mediated immune responses in the midgut protect mosquitoes from lethal bacterial infections, likely through AMP activity. However, we found no evidence of REL2 function in anti-PF responses. Instead, our results confirmed the contribution of midgut microbiota to the inter-experimental variability often observed in the outcomes of PF infections in mosquitoes.

## Serratia spp.-induced dysbiosis hinders survival and PF development in REL2-deficient mosquitoes

Depletion of REL2 leads to excessive bacterial growth and mosquito mortality. To identify the bacterial species responsible for these effects, we sequenced PCR amplicons of the variable region 4 (V4) of the 16S rDNA gene using DNA isolated from the midguts of WT and *REL2* mutant mosquitoes at 24 h after BF. To correlate microbial composition with survival rates, survival rates of the remaining mosquitoes from both experimental groups were also evaluated.
A

<figure><img src="image_16.png" /></figure>

Taxa
Serratia
Cedecea
Elizabethkingia
Klebsiella
Asaia
Aeromonas
Unknown
Other
Salmonella

B

<figure><img src="image_17.png" /></figure>

C

<figure><img src="image_18.png" /></figure>

D

<figure><img src="image_19.png" /></figure>

E

<figure><img src="image_20.png" /></figure>

F

<figure><img src="image_21.png" /><figcaption>Figure 4. Impact of *Serratia* spp. on *Anopheles* survival and *P. falciparum* infection in *REL2* mutants</figcaption></figure>

(A) Correlation between the survival of REL2⁻/⁻ mutants (left panel, *N* = 5, *n* = 60–135) and prevalence and relative abundance of *Serratia* in the mosquito midguts (right panel, *N* = 5, *n* = ~15) at 24 h post-blood feeding. Bar plots display the microbiome composition of individual mosquitoes.

(B) PCA plot illustrating the β diversity of individual microbiomes. Individual samples are color coded based on survival phenotype, and arrows indicate the most important taxa for each survival phenotype.

(C) α-diversity plots for microbiomes of mosquitoes with high (experiments 3 and 4) and low (experiments 1 and 2) survival rates. Dots show individual mosquitoes (*N* = 4, *n* = ~15). Boxplots indicate the median and IQR, and whiskers represent 1.5× IQR.

(D) Microbial association networks estimated using Pearson correlation coefficients for REL2⁻/⁻ mutants with high and low survival rates. The size of each node represents the eigenvector, and colors indicate relative abundance according to the heatmap. Positive correlations between the taxa are shown by green lines, and red lines indicate negative correlations.

(E and F) The effect of *Serratia* sp. Ag2 on mosquito survival and *P. falciparum* infection. WT and REL2⁻/⁻ mosquitoes were treated with antibiotics (AB), and one-half was colonized with *Serratia* sp. Ag2. (E) Kaplan-Meier survival analysis (*N* = 3, *n* = 23–76). Statistical significance was assessed by the log rank test, \*\*p < 0.0001. (F) Prevalence of *P. falciparum* infection; crossbars indicate medians of all experiments; lines connect medians of the experiments grouped by experimental condition (*N* = 6, *n* = 12–50). (C and F) Statistically significant differences were evaluated with the Wilcoxon rank-sum test (ns, *p* > 0.05; \*p < 0.05; \*\*p < 0.01).

A wide range of mosquito survival rates was observed in five experiments, ranging from very poor to very good (Figure 4A, left panel). A detailed analysis of bacterial communities from these experiments revealed a strong correlation between low survival rates and high prevalence and abundance of *Serratia* spp. (Figure 4A, right panel, experiments 1 and 2). In contrast,

mosquitoes from experiments with high survival rates (experiments 3, 4, albeit not experiment 5) showed more diverse microbial communities.

PCA of β-diversity of bacterial communities provided quantitative support to the correlation between mosquito mortality and occurrence of *Serratia*, *Aeromonas*, and *Elizabethkingia* spp.
(Figure 4B). Conversely, good mosquito survival was linked to the presence of *Asaia*, *Klebsiella*, and a species from the Enterobacteriaceae family. Overall, the β-diversity of the midgut bacteria varied between independent experiments but showed no difference between WT mosquitoes and *REL2* mutants within the same experiment (Figure S4A). In experiments with poor survival rates, *REL2* mutants were found to harbor higher bacterial loads than WT mosquitoes, as quantified by 16S PCR (Figure S4B).

The α-diversity indices measure species diversity within a single sample and are often used to assess the overall health and stability of the microbiome. Significant differences in the α-diversity index were observed in *REL2* mutants when comparing mosquitoes with low and high survival phenotypes (Figure 4C). These results suggested a correlation between mortality of the *REL2* mutants and reduced diversity of their midgut microbiomes, dominated by *Serratia*, *Aeromonas*, and *Elizabethkingia*.

As *Serratia* and *Elizabethkingia* species were identified in the midgut of *REL2* mutants, we examined whether the co-occurrence of these bacteria impacted mosquito survival. To do this, we performed a microbial association network analysis using microbial abundance data from the samples of REL2-deficient mosquitoes with high and low survival rates by focusing on the 30 most prevalent bacterial genera and calculating Pearson's correlation coefficients between them. Bacterial communities in the samples with low survival showed positive associations between the highly abundant *Serratia* and several rare taxa (Figure 4D). In contrast, the network linked to high survival showed strong associations among rare taxa, while moderately abundant *Serratia* and *Asaia* displayed weak connections. These results suggested that high *Serratia* abundance was a probable driver of the poor survival of *REL2* mutants and indicated that both genotype and microbiome composition contribute to the survival outcome.

To directly test the potential impact of *Serratia* spp. on mosquito mortality, we fed WT mosquitoes and *REL2* mutants with *Serratia* sp. Ag2 strain, which has been previously isolated from *A. gambiae*.⁴⁵ Successful colonization was confirmed by plating mosquito homogenates on agar plates and measuring CFUs. Two days after colonization, no significant differences were detected in bacterial loads between the WT and *REL2*⁻¹⁻⁰ mosquitoes (data not shown). While non-colonized control mosquitoes from both strains showed high survival rates, those colonized with *Serratia* sp. Ag2 experienced significantly higher mortality, particularly *REL2* mutants (Figures 4E and S4C). These results provided a causative link between *Serratia* presence in the midgut and high mortality in *REL2* mutants.

Previous field and laboratory studies have documented the impact of *Serratia* on *Plasmodium* infection.¹⁹,⁴⁶⁻⁴⁸ We examined how *Serratia* sp. Ag2 strain affected the susceptibility of WT and *REL2*⁻¹⁻⁰ mosquitoes to PF infection. Mosquitoes were colonized with *Serratia* sp. Ag2 and infected with PF. While similar infection prevalence was observed in WT and *REL2*⁻¹⁻⁰ mosquitoes treated with antibiotics, colonization with *Serratia* significantly reduced PF prevalence in *REL2* mutants compared to WT mosquitoes (Figure 4F). However, no significant differences were detected on oocyst loads (Figure S4D). These findings suggested that mosquito susceptibility to PF infection was caused by direct inhibition of parasite development in the

mosquito midgut by proliferating *Serratia* sp. Ag2 in REL2-deficient mosquitoes.

DISCUSSION

The immune system is essential for protecting organisms from daily threats posed by invading microbes. Studies in insects have traditionally focused on immune responses to experimental infections with model pathogens, often using artificial feeding or injection methods. Here, we explored the physiological role of the immune system after natural microbial expansion induced by BF. Our findings revealed a critical role of REL2 in maintaining midgut microbial homeostasis. Disruption of REL2 signaling led to a shift in bacterial communities, favoring *Serratia* spp., and increased mosquito mortality, highlighting the impact of environmental bacterial opportunists and the importance of effective immune responses in mosquito physiology. In contrast to earlier studies,³⁰,³⁴ but in line with more recent research,³³ our results do not support a direct role of REL2-mediated immune responses against PF parasites. Instead, they suggest that the midgut abundance of *Serratia* spp. directly impairs the efficiency of *Plasmodium* colonization in mosquito midguts.

Fat body-expressed AMP genes are the major targets of the *Drosophila* IMD pathway after a bacterial injection. Reported here transcriptional analyses of the cardia, the anterior and posterior midguts of *Anopheles* mosquitoes showed that BF induced REL2-mediated expression of AMPs only in the posterior midgut. In contrast, strong, constitutive expression of AMPs in the cardia was partially independent of REL2. While our results suggested that REL2-regulated AMP expression was crucial for maintaining bacterial homeostasis after a blood meal, further research is needed to determine specific roles of individual AMPs in counteracting dysbiosis caused by *Serratia* spp. overgrowth.

Until recently, the antibacterial properties of AMPs were believed to rely on a broad additive or synergistic mode of action. While some AMPs may indeed contribute to this broad activity,⁴⁹,⁵⁰ two specific *Drosophila* AMPs have been reported to exhibit antibacterial activity against particular bacterial species. For instance, dipticerin has been shown to be crucial in defenses against *Providencia rettgeri* but not other *Providencia* species.⁴⁹ Conversely, attacin protected the fruit flies against *Serratia marcescens*.⁵¹ Notably, our data showed that the expression of *Anopheles* attacin is not regulated by REL2. Consequently, this AMP is likely dispensable for protection against *Serratia*. Other REL2-dependent AMPs may mediate *Anopheles* resistance to *Serratia* overgrowth in the midgut.

Our findings revealed significantly higher AMP expression levels in the cardia and anterior midgut of *Anopheles* as compared to *Aedes* mosquitoes.⁵² It remains to be explored whether this observation reflects an evolutionary adaptation of the mosquito species to opportunistic pathogens. Another intriguing observation from this study is the potential involvement of REL2-regulated FBN proteins in midgut antibacterial responses. Although the expression of these FBNs has been reported to increase in response to bacterial infections, they have not yet been functionally characterized.⁵³⁻⁵⁵

Interestingly, we did not observe REL2-mediated positive feedback loop for the intracellular components of this signaling
pathway starting from the pathway receptor PGRP-LC to the negative regulator Caspar. Instead, we identified REL2-mediated regulation of potential upstream components, such as PGRPs and CTLs. These modulators of immune signaling can be classified as either positive or negative. Positive regulators bind to microbial PGNs and activate the pathway, while negative regulators catalyze or compete for PGNs to balance immune activation. PGRP-LB is one of the known negative regulators of REL2 signaling in the midgut.37 Additionally, the short PGRP-S2 and PGRP-S3 are predicted to have amidase activity, potentially hydrolyzing PGNs to attenuate the quantities of activating ligands. The specific expression of these short PGRPs in the midgut aligns with earlier reports suggesting that they are not essential for mosquito defenses against systemic bacterial infections in the hemolymph.34 Notably, RNAi-mediated knockdown of these genes moderately increased the prevalence of PF infections.37 In light of our findings, it is likely that the observed increase in PF prevalence may be due to the negative impact of bacterial dysbiosis on parasite development through the dysregulation of REL2 signaling. Unlike PGRP-S2 and -S3, PGRP-S1 lacks a conserved amidase signature and may activate the REL2 pathway by binding to PGN.56 Further studies are needed to confirm this hypothesis.

We also detected REL2-mediated expression of *CTL4* and *CTLMA2* in the anterior and posterior midgut. These lectins form a heterodimer in the hemolymph,57 which negatively regulates the melanization of *Plasmodium* parasites and fungi.58,59 Additionally, CTL4/CTLMA2 dimer protects *A. gambiae* against injected Gram-negative bacteria by an unknown melanization-independent mechanism.59,60 These two functions occur in the hemolymph and are REL2 independent. Our data indicate that CTL4/CTLMA2 heterodimer may potentially function as a negative regulator of REL2 signaling in the midgut, as *CTL4* knock-down was reported to decrease mosquito bacterial loads after BF.59 However, the molecular mechanisms underlying CTL4/CTLMA2-mediated regulation remain unknown.

RNA-seq analyses detected the minor impact of REL2 depletion on the expression of five GO-term annotated metabolic genes. However, the differences in their expression were low, and no specific metabolic pathway could be identified. Additionally, seven genes up-regulated in the *REL2* mutants were assigned to the cell adhesion GO term. These included *ninjurin* (AGAP006708) and a tight cluster of genes *AGAP029693*, *AGAP029698*, with *AGAP029696* and *AGAP029698* previously annotated as *DE-cadherin-like isoform X1* paralogs.61 The function of these genes is currently unknown, and it is unclear whether the REL2 pathway directly represses their transcription.

Due to the tissue-specific regulation of AMPs in the cardia, anterior, and posterior midgut, we propose a two-step model for the role of REL2 in limiting bacteria proliferation. High AMP expression in the anterior midgut likely functions as a pathogen filter, reducing bacterial entry into the midgut. In the absence of BF, bacterial loads in the posterior midgut remain low. However, during BF, bacterial proliferation produces large quantities of PGNs, which activate the REL2 pathway and trigger AMP synthesis. Increased AMP activity inhibits bacteria growth. A positive feedback loop within the pathway, which controls the expression of negative upstream regulators, terminates the signaling to restore tissue homeostasis. This model challenges the notion of immune

tolerance in the posterior midgut.52 Indeed, the REL2 pathway is crucial for mosquito survival after BF, despite constitutive AMP expression in the cardia. We propose that REL2 activity in both sections of the midgut is essential for maintaining microbial homeostasis. Consistent with previous reports,15,62-64 we also observed REL2-dependent responses in hemocytes and the fat body after the injection of a bacterial mix. However, at the population level, the role of such sepsis-like wounding in individual mosquitoes appears to be less life threatening compared to the bacterial proliferation that follows BF.

As we did not control bacterial exposure, our study mimicked inter-experimental variability of the environmental microbiome and allowed us to unravel the role of REL2 in controlling opportunistic pathogens such as *Serratia* spp. The role of *Serratia* in driving dysbiosis appears to be conserved across the dipteran order.65 Some strains of *S. marcescens* have been reported to damage the insect intestinal epithelium66,67 and cross into the hemolymph after ingestion.62,66 In *Anopheles*, the inhibition of *P. berghei* infection by *Serratia* has been linked to flagellar function66 and/or to the induction of host immunity.68 The relationship between these two phenotypes remains unclear. Soluble factors isolated from *S. marcescens* also inhibited *in vitro* asexual stages of PF69; however, their identity is unknown. In the reported experimental PF infections here, *Serratia* likely inhibited the fertilization of gametes and/or ookinete development, given that oocyst prevalence positively correlates with the efficiency of ookinete maturation.70

Unlike in *Drosophila*, where the IMD pathway regulates many immune genes,71 we identified only a few major target genes linking REL2 activity to bacterial detection and effector activation. We demonstrated that REL2 maintains bacterial homeostasis in the midgut and shapes the local environment encountered by the invading parasites. Understanding the precise mechanisms governing interactions among bacteria, mosquito hosts, and the pathogens they transmit holds great promise to uncovering the molecular mechanisms underlying the ecology of malaria transmission by mosquito vectors.

## Limitations of the study

While our study provides valuable conceptual insights, its findings are limited by the laboratory-specific environment and an overrepresentation of laboratory-enriched bacterial species. Future research should investigate a broader range of microbial interactions to better understand the role of REL2 in regulating both healthy microbial communities and *Plasmodium*-inhibiting bacteria in natural mosquito environments. Additionally, our assessment of the effects of BF, *Plasmodium* infection, and REL2 deficiency was restricted to 24 h post-feeding, which may have overlooked important gene regulation changes occurring at later time points in mosquito tissues.

## RESOURCE AVAILABILITY

### Lead contact

Requests for further information and resources should be directed to the lead contact, Elena A. Levashina (levashina@mpiib-berlin.mpg.de).

### Materials availability

Mosquito lines generated in this study will be made available upon request.
## Data and code availability

* Raw RNA sequencing data have been deposited at the NCBI repository: SRA: PRJNA1297429 with sample accession numbers: SRR34747078-SRR34747147.

* Raw V4 16S rDNA sequencing data have been deposited at the NCBI repository: SRA: PRJNA1297429 with sample accession numbers: SRR34732078-SRR34732219.

* Other data as well as processed V4 16S rDNA and RNA sequencing data and related code for analysis and visualization have been deposited to the Edmond repository https://doi.org/10.17617/3.WSRVSR.

* Raw whole-genome sequencing data have been deposited at the NCBI repository: SRA: PRJNA1299295.

* The assembled sequence of REL2 -/- locus reported here has been deposited at the Edmond repository, folder transgenic_lines https://doi.org/10.17617/3.WSRVSR.

* All custom codes required to generate and analyze the data were deposited at GitLab https://gitlab.mpcdf.mpg.de/vectorbiology/zakovic_et_al_2025 and given a DOI: https://doi.org/10.5281/zenodo.16567805.

## ACKNOWLEDGMENTS

The authors thank the EMBL sequencing facility and Dr. V. Benes for performing RNA sequencing and the Max Planck Sequencing Facility in Cologne for 16S rDNA sequencing and alignment. We also thank technical assistants of the Vector Biology Department: D. Eyermann and M. Andres for making the *P. falciparum* cultures, L. Spohr and M. Andres for *P. falciparum* infections, and C. Kreschel for the help with mosquito breeding. We are grateful to M. Erazo for the discussions regarding bacterial colonization and to Drs. G. Costa and T. Shan for critical reading of the manuscript. Finally, we thank current and past Vector Biology lab members for constructive discussions. S.Z.' work was supported by a Deutsche Forschungsgemeinschaft PhD fellowship (DFG-GRK2046). The project was supported in part by the collaborative CNRS grant "International Research Laboratory" (IRL) 2012-2017.

## AUTHOR CONTRIBUTIONS

E.A.L.: conceptualization, supervision, and funding acquisition. C.K. and E.M.: animal resources. S.Z., G.E.R., R.G., and C.G.M.: investigation. S.Z. and G.E.R.: data curation. S.Z.: formal analysis and visualization. E.A.L. and S.Z.: project administration, supervision, and writing.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS

During the preparation of this work, the authors used OpenAI ChatGPT 4.0 in order to improve the readability, grammar, and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

* Mosquito stocks

* *P. falciparum* cultures

* Bacterial cultures

* METHOD DETAILS

* Mosquito rearing

* *P. falciparum* infections

* Systemic bacterial injections

* Dissections

* RT-qPCR

* RNA-sequencing

* RNA-seq data analysis

* *DEF1* reporter imaging in dissected guts

* Mosquito survival

* Antibiotic treatment

* Quantification of bacterial loads

* 16S rDNA amplicon sequencing

* 16S-sequencing data analysis

* Colonization of mosquitoes with *Serratia* spp

* Whole genome sequencing and *de novo* assembly

* QUANTIFICATION AND STATISTICAL ANALYSIS

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.116282.

Received: March 31, 2025

Revised: July 11, 2025

Accepted: August 22, 2025

Published: September 12, 2025

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## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Bacterial strains</strong></td></tr><tr><td>Escherichia coli</td><td>IBMC, Strasbourg</td><td>NA</td></tr><tr><td>Staphylococcus aureus</td><td>IBMC, Strasbourg</td><td>NA</td></tr><tr><td>Serratia sp. Ag2</td><td>BEI resources<sup>45</sup></td><td>NR-50123</td></tr><tr><td colspan="3"><strong>Chemicals</strong></td></tr><tr><td>Agarose</td><td>Sigma-Aldrich</td><td>A9539</td></tr><tr><td>Citrate Buffer</td><td>Life Technologies</td><td>005001</td></tr><tr><td>Chloroform</td><td>Sigma-Aldrich</td><td>372978</td></tr><tr><td>dNTP-mix (2mM)</td><td>Thermo Fisher Scientific</td><td>R0193</td></tr><tr><td>Ethanol, molecular grade</td><td>Carl Roth</td><td>1HPH-1</td></tr><tr><td>Ethidium bromide</td><td>Sigma-Aldrich</td><td>E1510</td></tr><tr><td>Fetal Bovine Serum</td><td>Sigma-Aldrich</td><td>F6178</td></tr><tr><td>Gentamicin</td><td>Sigma-Aldrich</td><td>G1397</td></tr><tr><td>Luria broth (LB) agar</td><td>Carl-Roth</td><td>X965.2</td></tr><tr><td>Liver Powder</td><td>NOW Foods</td><td>NA</td></tr><tr><td>Water, DEPC-treated</td><td>Thermo Fisher Scientific</td><td>R0601</td></tr><tr><td>Mercurochrome</td><td>Sigma-Aldrich</td><td>M7011</td></tr><tr><td>Oligo(dt)30</td><td>Qiagen</td><td></td></tr><tr><td>Penicillin-Streptomycin (10.000 U/ml)</td><td>Thermo Fisher</td><td>15140122</td></tr><tr><td>Phosphate-buffered saline (PBS) 10x</td><td>Invitrogen</td><td>AM9624</td></tr><tr><td>Random Hexamer Primers (0.2µg/µL)</td><td>Fermentas</td><td>SO142</td></tr><tr><td>RNAzol reagent</td><td>Sigma-Aldrich</td><td>R4533</td></tr><tr><td>RNAse-free PBS</td><td>Thermo Fisher Scientific</td><td>AM9625</td></tr><tr><td>Schneider's Insect Medium</td><td>Sigma-Aldrich</td><td>S0146</td></tr><tr><td>Template switching oligo (TSO)</td><td>IDT dna</td><td>NA</td></tr><tr><td>TRIzol reagent</td><td>Thermo Fisher Scientific</td><td>10296010</td></tr><tr><td>Tuna Meal</td><td></td><td></td></tr><tr><td>TE-Buffer</td><td>Thermo Fisher Scientific</td><td>12090015</td></tr><tr><td>Vanderzant insect vitamin mixture</td><td>Sigma-Aldrich</td><td>V1007</td></tr><tr><td colspan="3"><strong>Critical commercial assays</strong></td></tr><tr><td>dsDNA 910 Reagent Kit</td><td>Agilent technologies</td><td>DNF-910-K1000</td></tr><tr><td>HS NGS Fragment Kit</td><td>Agilent technologies</td><td>DNF-474</td></tr><tr><td>Direct-Zol RNA microprep kit</td><td>Zymo Research</td><td>R2062</td></tr><tr><td>Fast SYBR Green qPCR Master Mix</td><td>Applied Biosystems</td><td>4385614</td></tr><tr><td>GoTaq G2 Green Master Mix</td><td>Promega</td><td>M7823</td></tr><tr><td>KingFisher Acessory Kit</td><td>Macherey-Nagel</td><td>744950</td></tr><tr><td>Nextera XT DNA Library Prep. Kit</td><td>Illumina</td><td>FC-131-1096</td></tr><tr><td>Nextera XT Index Kit</td><td>Illumina</td><td>FC-131-1002</td></tr><tr><td>Nucleo MagVet Kit</td><td>Macherey-Nagel</td><td>ML-8828</td></tr><tr><td>PrimeTime Master Mix</td><td>Integrated DNA Technologies</td><td>1055772</td></tr><tr><td>ReverseertAid H Minus First Strand</td><td>Thermo Fisher Scientific</td><td>K1631</td></tr><tr><td>Qubit RNA BR assay kit</td><td>Thermo Fisher Scientific</td><td>Q10211</td></tr><tr><td>Qubit RNA HS assay kit</td><td>Thermo Fisher Scientific</td><td>Q32855</td></tr><tr><td>RapidOut DNA Removal Kit</td><td>Thermo Fisher Scientific</td><td>K2981</td></tr><tr><td>SuperScript III kit</td><td>Thermo Fisher Scientific</td><td>18080044</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Deposited data</strong></td></tr><tr><td>Raw RNA-sequencing data</td><td>This paper</td><td>SRA: PRJNA1297429. Sample accession numbers: SRR34747078-SRR34747147 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1297429</td></tr><tr><td>Processed RNA-sequencing data</td><td>This paper</td><td>Edmond repository: https://doi.org/10.17617/3.WSRVSR; folder rna-seq/data_files</td></tr><tr><td>Raw 16S-sequencing data</td><td>This paper</td><td>SRA: PRJNA1297429. Sample accession numbers SRR34732078-SRR34732219 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1297429</td></tr><tr><td>Processed 16S-sequencing data</td><td>This paper</td><td>Edmond repository: https://doi.org/10.17617/3.WSRVSR; folder microbiome/data_files</td></tr><tr><td>Raw WGS data</td><td>This paper</td><td>SRA: PRJNA1299295 http://www.ncbi.nlm.nih.gov/bioproject/1299295</td></tr><tr><td>Assembled REL2<sup>-/-</sup> locus</td><td>This paper</td><td>Edmond repository: https://doi.org/10.17617/3.WSRVSR; transgenic_lines/tig00006913_entire_contig.fasta</td></tr><tr><td>A. gambiae PEST genome</td><td>VectorBase Consortium<sup>72</sup></td><td>https://vectorbase.org/</td></tr><tr><td>SILVA</td><td>Quast et al.<sup>73,74</sup></td><td>https://www.arb-silva.de/</td></tr></tbody></table>

Experimental models: Organisms/strains

<table><tr><td>A. gambiae, Strain G3</td><td>Harvard</td><td>NA</td></tr><tr><td>A. gambiae, Strain G3, REL2<sup>-/-</sup></td><td>This paper</td><td>NA</td></tr><tr><td>A. gambiae, Strain G3, DEF1::GFP</td><td>This paper</td><td>NA</td></tr><tr><td>A. gambiae, Strain G3, vasa::Cas9</td><td>IBMC Strasbourg<sup>53,75</sup></td><td>NA</td></tr><tr><td>A. gambiae, Strain G3, vasa::Cre</td><td>IBMC Strasbourg<sup>42</sup></td><td>NA</td></tr></table>

Oligonucleotides

<table><tr><td>5'-GGGGAGGGTAGCTCGTGGGTT-3'</td><td>Eurofins</td><td>REL2<sup>-/-</sup> genotyping Forward</td></tr><tr><td>5'-GCTGCTGCTGTTGAAGTTGTTG-3'</td><td>Eurofins</td><td>REL2<sup>-/-</sup> genotyping Reverse</td></tr><tr><td>Illumina adapter - GTGCCAGCMGCCGCGGTAA</td><td>Caporaso et al.<sup>76</sup></td><td>515 F, 16S V4</td></tr><tr><td>Illumina adapter - barcode - GGACTACHVGGGTWTCTAAT</td><td>Caporaso et al.<sup>76</sup></td><td>806 R, 16S V4</td></tr><tr><td>5'-CCACCAAACAACCAACAAC-3'</td><td>idtdna</td><td>CEC1 Forward</td></tr><tr><td>5'-GCCGGCTCCCTCAATTT-3'</td><td>idtdna</td><td>CEC1 Reverse</td></tr><tr><td>6-FAM-5'-TCTTTCCCAGCTTCTTCAGCCGTC-ZEN-3' Iowa Black</td><td>idtdna</td><td>CEC1 Probe</td></tr><tr><td>5'-ACTTCAAGCTGATCTTTCTCGT-3'</td><td>idtdna</td><td>CEC2 Forward</td></tr><tr><td>5'-TGCGCCTTCCACTTTCTT-3'</td><td>idtdna</td><td>CEC2 Reverse</td></tr><tr><td>6-FAM-5'-CTGGTACTGATGGCTGCGTTCCTG-ZEN-3' Iowa Black</td><td>idtdna</td><td>CEC2 Probe</td></tr><tr><td>5'-GGCAAGCGGTTGGAGAA-3'</td><td>idtdna</td><td>CEC3 Forward</td></tr><tr><td>5'-GATCAGTCAGCCACATCCTATC-3'</td><td>idtdna</td><td>CEC3 Reverse</td></tr><tr><td>6-FAM-5'-CAATGTGTTTCGGCCGGCTAAGAA-ZEN-3' Iowa Black</td><td>idtdna</td><td>CEC3 Probe</td></tr><tr><td>5'-CCAATCTCAATACCCTTCTGGA-3'</td><td>idtdna</td><td>DEF1 Forward</td></tr><tr><td>5'-CGCTTGGCCCGATAGTT-3'</td><td>idtdna</td><td>DEF1 Reverse</td></tr><tr><td>6-FAM-5'-TGCCCGAGGAAACGCACCAT-ZEN-3' Iowa Black</td><td>idtdna</td><td>DEF1 Probe</td></tr><tr><td>5'-CCAAGATGTCGGGCAAGTAT-3'</td><td>idtdna</td><td>FBN9 Forward</td></tr><tr><td>5'-AATCGAGCGCACCTTAAA-3'</td><td>idtdna</td><td>FBN9 Reverse</td></tr><tr><td>6-FAM-5'-CGGTGGTTGGTTGGTGATACAGCA-ZEN-3' Iowa Black</td><td>idtdna</td><td>FBN9 Probe</td></tr><tr><td>5'-GCGGAGTATATAGCCGATGTG-3'</td><td>idtdna</td><td>TEP1 Forward</td></tr></table>

(Continued on next page)
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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>5'-GATTTGGTATAGCTGAGATCCGT-3'</td><td>idtdna</td><td>TEP1 Reverse</td></tr><tr><td>6-FAM-5'-TACAATGTGGCCAACCAGACCGAG-ZEN-3' Iowa Black</td><td>idtdna</td><td>TEP1 Probe</td></tr><tr><td>5'-CGGACAGACGACCATCAATAG-3'</td><td>idtdna</td><td>GAMB Forward</td></tr><tr><td>5'-GAGCTCCACTCAGAAGAAACA-3'</td><td>idtdna</td><td>GAMB Reverse</td></tr><tr><td>6-FAM-5'-CGTCTGAACAGCGGCCAAACTTTC-ZEN-3' Iowa Black</td><td>idtdna</td><td>GAMB Probe</td></tr><tr><td>5'-CATCGAACACAAAGTTGACAC-3'</td><td>idtdna</td><td>RPS7 Forward</td></tr><tr><td>5'-CTAGCACGCAACCCTTATATT-3'</td><td>idtdna</td><td>RPS7 Reverse</td></tr><tr><td>6-FAM-5'-TACAAGAAGCTGACTGGCCGTGAC-ZEN-3' Iowa Black</td><td>idtdna</td><td>RPS7 Probe</td></tr><tr><td>5'-TCCTACGGGAGGCAGCAGT-3'</td><td>idtdna</td><td>16S Forward</td></tr><tr><td>5'-GGACTACCAGGGTATCTAATCCTGTT-3'</td><td>idtdna</td><td>16S Reverse</td></tr><tr><td>6-FAM-5'-CGTATTACCGCGGCTGCTGGCAC-ZEN-3' Iowa Black</td><td>idtdna</td><td>16S Probe</td></tr></tbody></table>

Recombinant DNA

<table><tr><td>536 pDSAY-REL2_plasmid.dna</td><td>This paper</td><td>Edmond repository:<br>https://doi.org/10.17617/3.WSRVSR; folder<br>transgenic_lines</td></tr><tr><td>pCam107otT_DEF1_reporter_plasmid.gb</td><td>This paper</td><td>Edmond repository:<br>https://doi.org/10.17617/3.WSRVSR; folder<br>transgenic_lines</td></tr></table>

Software and algorithms

<table><tr><td>R</td><td>R Core Team<sup>77</sup></td><td>https://www.R-project.org/</td></tr><tr><td>R decontam</td><td>Davis et al.<sup>78</sup></td><td>https://benjjneb.github.io/decontam/</td></tr><tr><td>bwa</td><td>Heng Li<sup>79</sup></td><td>https://github.com/lh3/bwa</td></tr><tr><td>FastQC</td><td>Babraham Bioinformatics<sup>80</sup></td><td>https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td></tr><tr><td>DESeq2</td><td>Love et al.<sup>81</sup></td><td>https://bioconductor.org/packages/release/bioc/html/DESeq2.html</td></tr><tr><td>phyloseq</td><td>McMurdie et al.<sup>82</sup></td><td>https://joey711.github.io/phyloseq/</td></tr><tr><td>Seurat</td><td>Butler et al.<sup>83</sup></td><td>https://satijalab.org/seurat/</td></tr><tr><td>microViz</td><td>Barnett et al.<sup>84</sup></td><td>https://david-barnett.github.io/microViz/</td></tr><tr><td>NetCoMi</td><td>Peschel et al.<sup>85</sup></td><td>https://netcomi.de/</td></tr><tr><td>Inkscape</td><td>Inkscape Project<sup>86</sup></td><td>https://inkscape.org/</td></tr><tr><td>Canu</td><td>Koren et al.<sup>87</sup></td><td>https://canu.readthedocs.io/en/latest/</td></tr><tr><td>Porechop</td><td>Wick et al.<sup>88</sup></td><td>https://github.com/rrwick/Porechop</td></tr><tr><td>Filtlong</td><td></td><td>https://github.com/rrwick/Filtlong</td></tr><tr><td>SnapGene</td><td></td><td>https://www.snapgene.com/</td></tr></table>

Other

<table><tr><td>Data analyses and resources related to this publication</td><td>This study</td><td>Edmond repository: https://doi.org/10.17617/3.WSRVSR<br>GitLab: https://doi.org/10.5281/zenodo.16567805</td></tr></table>

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Mosquito stocks

A. gambiae G3 strain was used as a wild-type (WT) control and for creation of the REL2-/- mutant line.

The REL2-/- mutant line was generated through a two-step process using the CRISPR/Cas9 and Cre-recombinase systems to insert a 3xP3::GFP fluorescence cassette into the coding sequence of the REL2 gene (Figure S1A). A plasmid was constructed to
express a guide RNA under the control of the AGAP013557 *U6* promoter targeting the 5'-GCAGCAGCAGTGGTCAGTGT-3' sequence in the second exon of *REL2*. This plasmid also included *REL2* 3'- and 5'-UTR homology regions and a compound 3xP3::YFP and eGFP cassette. The plasmid was injected into embryos of an *A. gambiae* line expressing the *vasa::Cas9* transgene.<sup>53,75</sup> A single YFP-positive larva, with the entire plasmid inserted into the *REL2* locus, was used to establish the mutant line. Subsequently, the YFP marker gene, flanked by *loxP* sites, was excised using *Cre* recombinase by crossing the YFP-positive mutants with a *Cre*-expressing line.<sup>42</sup> This process positioned the 3xP3 promoter in front of the eGFP fluorescence marker, resulting in knock-in larvae with GFP expression in the nervous system, including the eyes. The knock-in efficiency was low, as the mutant line was derived from a single recovered insertion event. An improved protocol for the CRISPR/Cas9 knock-in procedure is available at https://hal.science/hal-04380430v1.

Between each step, transgenic mosquitoes were outcrossed to the wild type (WT) mosquitoes to select against individuals carrying *vasa::Cas9* or *vasa::Cre* transgenes. The final *REL2*<sup>-/-</sup> line was back-crossed with WT mosquitoes for more than seven generations. *REL2*<sup>-/-</sup> homozygous stage 1 larvae (L1) were sorted based on GFP fluorescence using the Complex Object Parametric Analyzer and Sorter (COPAS) and the homozygosity of resulting adults was confirmed by genotyping (Figures S1B and S1C).

The correct integration of the insert was verified using Oxford Nanopore long read sequencing and the map of the modified *REL2* locus is shown in the Figure S1D.

To generate the transgenic *DEF1::GFP* reporter line, 1,341 bp of the *DEF1* (AGAP011294) 5'-UTR sequence including the endogenous *DEF1* initiator codon (ATG) and 353 bp of the *DEF1* 3'-UTR transcription terminator sequence were cloned upstream and downstream of the *GFP* coding sequence, respectively, within a *piggyBac* vector containing *3xP3::dsRed* and *OplE2::pac* (puromycin resistance gene) transgenesis selection markers. Mosquito transgenesis in the G3 background was performed as described previously.<sup>42</sup>

## P. falciparum cultures

*P. falciparum* NF54 parasites were cultured at 37°C with 3% *O₂* and 4% *CO₂*. Asexual blood stages were maintained in a complete culture media (RPMI 1640 medium with L-glutamine, 25 mM HEPES (ThermoFisher), 10 mM hypoxanthine (c-c-pro GmbH), 20 µg/mL gentamicin (Sigma), supplemented with 10% A+ human serum (Haema) in O+ human erythrocytes (Haema) at 2–5% haematocrit. Media was changed daily and the cultures were diluted every two to three days to maintain the parasitaemia below 3–4%. To obtain gametocytes for the infection, the asexual stages were diluted with fresh red blood cells to 1% parasitaemia and 4% haematocrit in 6 mL complete medium without gentamicin. Gametocytes were cultured for 14 days with daily medium changes. The number of mature gametocytes was evaluated using a cell counting chamber (Neubauer). Mature gametocyte cultures (15–16 days post culture set-up) were harvested by centrifugation and mixed with pre-warmed human red blood cells and serum to obtain final gametocytemia of 0.15%–0.30% at 45% haematocrit.

## Bacterial cultures

*Escherichia coli* and *Staphylococcus aureus* strains, obtained from IBMC Strasbourg, were used for systemic bacterial injections. For mosquito colonization, *Serratia* sp. Ag2 strain, isolated from *Anopheles* mosquitoes by Jiannong Xu, and obtained from BEI resources<sup>45</sup> was used. The details of the bacterial growth are described in the *method details* section of the *STAR Methods*.

## METHOD DETAILS

### Mosquito rearing

Mosquitoes were reared at 28°C 80% relative humidity, with 12 h day-night cycles. WT and *REL2*<sup>-/-</sup> larvae were reared in the same breeding water to allow the sharing of microbial communities, separated by a mesh window, unless stated otherwise. Larvae were fed with tuna-liver food mix, adults were fed with 10% sucrose solution, *ad libitum*. Three-to four-day-old females were used for experiments. Mosquitoes which only received sucrose meals, but not blood-meals, were called unfed throughout the manuscript. Blood feeding of mosquitoes was done with human donor blood using artificial membrane feeder system. Only fully engorged females were kept for experiments. The same reconstituted blood was used for non-infectious and infectious blood feedings with *P. falciparum* when experiments were done in parallel.

### P. falciparum infections

The blood meal containing *P. falciparum* NF54 gametocytes was provided in the S3 facility using an artificial membrane feeder system. The mosquitoes were kept at 26°C 80% relative humidity. On day 11 after infection, infected mosquitoes were sacrificed in 70% ethanol, washed twice in 1 x PBS and dissected in 1% mercurochrome in *H₂O*. Oocysts were counted under a light microscope.

### Systemic bacterial injections

One colony of *E. coli* or *S. aureus* was picked from agar plates and grown overnight in 5 mL Luria Broth (LB) medium on a shaker at 37°C and 250 rpm rotation. In the morning, liquid cultures were centrifuged at 4,000 x g for 5 min and washed twice in 1 x PBS. Optical density (OD) was measured at 600 nm in 1:1 dilution and a dose for mosquito injections was adjusted with PBS to OD<sub>600</sub> = 0.1 for *E. coli* and OD<sub>600</sub> = 0.05 for *S. aureus*. Female mosquitoes were anesthetized on ice and injected into the thorax with 69 nL of bacterial
mix or PBS as control using Nanoliter Injector Nanoject II. Injected mosquitoes were transferred to 200 mL paper cups covered with a net, with 10% sucrose cotton pad on top for feeding *ad libitum*, and kept for 4 h in an insectary at 26°C and 80% relative humidity.

## Dissections

Hemocytes were collected by perfusion into a low-binding microcentrifuge tube (Eppendorf) containing 30 µL of insect medium consisting of Schneider's insect medium, citrate buffer and fetal bovine serum in ratio 6:3:1 as described in40. Briefly, mosquitoes were injected into the thorax with 690 nL of the insect medium, using the Nanoliter Injector Nanoject III, and left on ice for 20 min to allow hemocytes to detach from abdominal tissues. Later, a small incision was made with the surgical scissors in the second last abdominal segment. Complete volume of the needle was injected into the animal and the solution with hemocytes was collected with a low-bind pipette tip and placed into a 30 µL of the insect medium in a 2 mL microcentrifuge tube.

Midguts were dissected in a drop of RNase free PBS by gently pulling off the last abdominal segment, and were divided with a sterile needle at the transition between the thin anterior and thick sac-like posterior midgut.

For the fat body dissection, mosquitoes were pinned to a dissection dish, coated with Steinel hot melt glue, in 50 µL of cold RNase free PBS. Abdomen was opened along the ventral axis, and its sides were pinned to the dissection dish, exposing the internal organs. Fat body was aspirated using Nanoliter Injector and ejected into a microcentrifuge tube.

Abdominal carcass was separated from the rest of the body, after midgut removal. Carcasses were cleaned from other organs and placed into 2 mL microcentrifuge tube.

## RT-qPCR

Four hours after bacterial or PBS challenge, mosquito tissues were dissected as described above. Pools of tissues from 10 mosquitoes were collected per sample for the carcass, midgut and hemocytes. RNAzol (500 µL) was added to each sample, the samples were homogenized with Precellys bead beater at 6,000 x rpm twice for 30 s, with a 30 s break, centrifuged and kept at -80°C until RNA extraction. Total RNA was isolated with DirectZol RNA micro kit, according to manufacturer's instructions. RNA concentration was measured using Qubit. Total RNA was reverse-transcribed with SuperScript III Kit according to the manufacturer's instructions.

Unfed, blood fed and *P. falciparum* infected mosquitoes were collected 24 h after blood feeding in 500 µL of Trizol reagent (N = 3, n = 1) and pools of 10 whole mosquitoes per condition were homogenized with Precellys bead beater (8,000 rpm twice for 30 s, with a 30 s break). DNA was digested using the RapidOut DNA Removal Kit. Total RNA (2 µg) was reverse-transcribed in a reaction mix, including dNTPs (2 mM), RevertAid H Minus Reverse Transcriptase (200 U/µL), RevertAid H Minus Reverse Transcriptase Buffer (5x), Hexamer Primers (100 µM), and Ribolock (20 U/µL). Quantitative PCR was performed in a 15 µL volume, containing 7.5 µL FAST SYBR green master mix, 4.5 µL of forward and reverse primers mix in H₂O and 0.6 ng of cDNA template.

## RNA-sequencing

Pools of tissues isolated from five mosquitoes were collected per sample for the fat body and midgut sections, while hemocytes were extracted from 10 mosquitoes. Samples were homogenized in RNAzol (500 µL) with Precellys bead beater at 6,000 x rpm twice for 30 s, with a 30 s break, centrifuged and kept at -80°C until RNA extraction. Total RNA was isolated with DirectZol RNA micro kit and RNA concentration was measured using Qubit. Due to limited RNA quantities for hemocyte samples, cDNA library was prepared using a modified protocol for single-cell RNA sequencing with 20 ng of RNA⁸⁹ First strand was reverse-transcribed using oligo-dT₃₀ primers. Template Switching Oligos (TSO) were attached to the 5'-end during first strand synthesis. Pre-amplification was performed for 10 cycles. Quality control was done with the NGS High Sensitivity kit DNF-474-33 (Agilent Fragment Analyzer). Libraries were prepared with Nextera XT DNA Library Preparation Kit. Library enrichment PCR was done with 10 cycles of amplification.⁸⁹ Following sample processing and quality evaluation, successful cDNA amplification was achieved for all but one hemocyte (*REL2*⁻¹⁻UF) and one anterior midgut sample (WT UF).

Sequencing was performed using NextSeq 150-bp pair-end protocol by EMBL sequencing facility. Alignment of the reads to reference genome AgamP4.11 was done with bwa mem aligner⁷⁹. FastQC program was used for quality control.⁸⁰ Gene annotation was performed with *GenomicFeatures*⁹⁰ using the Anopheles-gambiae-PEST_BASEFEATURES_AgamP4.12.gtf.gz* annotation file, obtained from VectorBase.⁷² Count matrix was obtained with *GenomicAlignments* package.⁹⁰ The sequenced samples generated approximately 4x10⁶ reads each, with the exception of the three samples of *P. falciparum*-infected mosquitoes, one anterior midgut (*REL2*⁻¹⁻) and two hemocyte samples (WT and *REL2*⁻¹⁻) that were excluded from analyses due to low quality and read count (< 300,000). For the samples that had passed quality control, transcriptome analyses identified around 10,000 genes per sample.

## RNA-seq data analysis

Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) was performed with *seurat* package.⁸³ Genes that had sum of counts <10 were removed from the count matrix. For creating the seurat object, and clustering, following parameters were used: min cells = 3, min features = 200, k parameter = 8, resolution = 0.5, dimensions = 14.

Genes that were enriched in each tissue were extracted. Filtering was performed based on following parameters: logfcMX threshold = 0.25, *P*adj < 0.05, min.pct = 0.25. For plotting the heat plots with gene-marker hits, normalized counts (expressed in natural logarithm) for each tissue were extracted, mean expression for each gene was calculated per strain, tissue and condition, and plotted with *ComplexHeatmap* package in R.⁹¹,⁷⁷
Differential gene expression analysis was performed on individual tissues with *DESeq2*.⁸¹ Gene counts were normalized by applying sample-specific size factors to each sample, determined by median ratio method and were transformed with variance stabilizing transformation with *DESeq2*. Principal Component Analysis (PCA) was performed for top 500 variable genes per tissue.

Samples of wild-type mosquitoes (WT) collected 24 h after blood feeding were used to identify tissue-specific differentially expressed genes (DEGs). Transcript levels in unfed (UF) and blood-fed (BF) samples were compared, and statistically significant DEGs ($P_{adj} < 0.05$) were subjected to Gene Ontology (GO) analyses.

To identify DEGs regulated by REL2 in unfed, blood fed, and *Plasmodium*-infected mosquitoes, transcript levels were compared between WT and *REL2*⁻/⁻ mutants in samples of dissected cardia+anterior midgut and posterior midgut. Statistically significant DEGs ($P_{adj} < 0.05$) were subjected to GO analyses. Subsequently, normalized counts extracted from the *DESeq2* object were used to identify REL2-regulated DEGs after *P. falciparum* infection, by comparing their expression to the blood fed condition.

For Gene Ontology (GO) enrichment analysis, a custom *A. gambiae* database with GO terms was constructed using VectorBase annotations, and DEGs were screened for enrichment. Statistical significance of identified GO terms was corrected for multiple-testing using Benjamini-Hochberg correction (*GOATOOLS*, $P_{adj} < 0.05$).⁹²

## DEF1 reporter imaging in dissected guts

*REL2*⁻/⁻ mutants were crossed with transgenic mosquitoes expressing the fluorescence reporter *Defensin1::GFP*.⁴² The *F*₂ generation was COPAS sorted⁹³ to establish lines homozygous for the reporter gene, identified by dsRed fluorescence in the eyes, and heterozygous for the *REL2* mutation, marked by GFP in the eyes. In subsequent generations, another round of COPAS sorting based on GFP fluorescence was conducted to produce mixed progenies of 50% control +50% *REL2*⁻/⁻ homozygous mosquito larvae, all homozygous for the reporter construct. The resulting adult mosquitoes were anesthetized on ice and sorted into *REL2* homozygous and heterozygous mutants and WT based on levels of GFP fluorescence in their eyes. The midguts of the selected unfed mosquitoes were dissected in PBS and live-imaged under a Zeiss SMZ18 fluorescence binocular microscope, to reveal the GFP fluorescence patterns of the *Defensin1* reporter in the midgut.

## Mosquito survival

Kaplan-Meier estimate of mosquito survival was calculated from day seven to eleven after blood feeding and plotted in *R* with *survival* and *survminer* incorporated in *ggquickeda* package.⁹⁴,⁹⁵

## Antibiotic treatment

Control female mosquitoes received 10% sucrose, while sucrose pads of the antibiotic treatment groups were supplemented with 75 µg/mL gentamicin sulfate and 100 U/mL penicillin/streptomycin mix, for feeding *ad libitum* from three days after emergence until blood-feeding.

## Quantification of bacterial loads

Mosquitoes were surface-sterilized in 70% ethanol for 30 s and washed in PBS three times. Individual mosquitoes (n = 10 per strain and condition) were homogenized in 100 µL of PBS. Serial dilutions were prepared and 50 µL were plated on LB agar plates. The plates were kept at 37°C overnight and the colony forming units were counted on the next day.

## 16S rDNA amplicon sequencing

Midgut dissections were performed in sterile conditions. Mosquitoes were sacrificed 24 h after blood feeding, collectively washed twice in 70% ethanol for 30 s and three times in sterile PBS, and three times individually in PBS. Forceps were cleaned in soapy water and 70% ethanol and sterilized by the Bunsen flame between each sample. Dissected midguts were homogenized with 100 µL of TE buffer and sterile sand with Precellys bead beater at 10,000 rpm twice for 30 s, with a 30 s break. Proteinase K 20 mg/mL in lysis buffer was added to each homogenate and left over-night digestion at RT. DNA was extracted with NucleoMag VET kit for bacterial and viral DNA, according to the protocol.

Variable region 4 (V4) of 16S rDNA gene was amplified in triplicates in 25 µL, with 10 µL of 5 Prime HotMasterMix. Barcoded primers with Illumina adapters were used⁷⁶ with 1 µL of DNA template per replicate. The amplicon concentration was assessed with the Agilent Fragment Analyzer and 240 ng of each sample was sequenced paired-end, at the Max Planck Sequencing Facility in Cologne on Illumina HiSeq 2,500 instrument.

## 16S-sequencing data analysis

Variable region 4 (V4) of 16S rDNA amplicon sequences were processed by *dada2* package⁹⁶ in R. Forward reads were trimmed at 240 bp and reverse - at 200 bp. Silva database, (v. 138) was used to assign species taxonomy to the amplicon sequencing variants (ASV).⁷³,⁷⁴ The resulting ASV count table, taxonomy and metadata were uploaded into a *phyloseq* object.⁸² Samples with less than 30,000 reads and taxa with unknown phyla were removed. *R* *decontam* package was used to remove contaminant taxa.⁷⁸ These were classified based on frequency they appeared in the low-versus high-concentration samples.

Alpha diversity was analyzed on non-transformed data. The count matrix filtering based on abundance and prevalence, retained 179 ASVs for further analysis. The data was converted to compositional with *microbiome* package,⁹⁷ and agglomerated on the genus
level for abundance plotting. Principal component analysis (PCA) was performed with microViz package<sup>84</sup> after centered-log (crl) transformation of the data.<sup>98,99</sup> Pearson's correlation coefficients between the 30 most frequent taxa were calculated and networks inferred with *netCoMi* package.<sup>85</sup> The union of the two networks was taken as the layout to facilitate the comparison. The size of the nodes corresponds to the eigenvector values of the taxa in each network. The abundance values were used to assign the colors (viridis palette). For this purpose, the mean of clr counts was calculated for each genus in both networks, the viridis palette colors were assigned to each value, after which the coloring was manually performed in Inkscape.<sup>86</sup>

## Colonization of mosquitoes with Serratia spp

WT and *REL2*⁻/⁻ larvae were reared separately and the pupae (*n* = 300) were cleaned in 500 mL of sterile water in 100 µm cell strainer. All equipment was sterilized by autoclaving or UV-light. Adults were given 10% sterile sugar pads supplemented with antibiotic mix (AB) containing penicillin/streptomycin (100 U/mL) and gentamicin sulfate (50 mg/mL) for two days, followed by sterile sugar pad for one day. Control group (AB control) continued feeding on sterile 3% sugar pads, while *Serratia* group was given 3% sugar pads inoculated with *Serratia* spp. cultures grown at 26°C (OD₆₀₀ = 0.5, corresponding to 1,5x10⁸ bacteria cells). Sterile- or *Serratia*-inoculated sugar pads were given to mosquitoes to feed *ad libitum* for two days. Sugar pads were changed daily. For CFU counting, 3–5 mosquitoes after antibiotic treatment and before blood feeding were homogenized in 100 µL PBS at 6,000 rpm for 10 s. Two serial dilutions were plated (50 µL) on LB agar plates.

## Whole genome sequencing and de novo assembly

Adult females were utilized for whole genome sequencing using two methods: Oxford Nanopore long-read (*n* = 100) and Illumina short-read sequencing (*n* = 10) (Novogene). High molecular weight DNA extraction and library preparation were conducted by Novogene. The quality of the sequencing reads was evaluated with *Nanoplot*, adapters were trimmed with *Porechop* and filtering was performed with *Filtlong*.<sup>88,100</sup> Read correction and assembly were carried out using *Canu*<sup>87</sup> (with ovlMerThreshold set to 1,000). The assembly was polished with *Pilon*<sup>101</sup> using Illumina short read sequences, generated on the NovaSeq X Plus platform, with paired-end 150 bp reads. The contig of interest was extracted from the long-read assembly using BLAST, aligned and annotated with *SnapGene* based on the *REL2* locus annotation (VectorBase,<sup>72</sup> Release 68, https://vectorbase.org), including the surrounding genes.

## QUANTIFICATION AND STATISTICAL ANALYSIS

Sample size: Throughout the manuscript, “N” refers to the number of independent replicates (i.e., biologically independent experiments), whereas “n” denotes the number of individual samples examined per replicate. Ranges (e.g., *n* = 12–30) indicate the minimum (12) and maximum (30) number of individuals per replicate for N replicates. For RNA extraction, each sample consists of a pool of 5 or 10 whole mosquitoes or a pool of mosquito tissues isolated from 5 or 10 individuals. The number of mosquitoes or tissues in each pool is specified for each replicate individually.

The statistical analysis and the graphical representation of the data were performed in R. Details on data analyses and R packages used are described in the *method details* section of the *STAR Methods*. Unpaired statistical tests were used throughout the study. Statistically significant differences in expression data generated by qPCR we evaluated by Student's t-test. The differences in survival were examined with Log rank test. Statistically significant differences in *P. falciparum* infections were evaluated with Wilcoxon Rank-Sum test. These details are also provided in the figure legends.

Experimental repeats with less than 10 mosquitoes available to count the *P. falciparum* infection outcome, were excluded from the analyses. Three samples were removed from the RNA-seq dataset due to poor quality, as described in the *method details* section of the *STAR Methods*.