Comparative RNA-Seq analyses of Drosophila plasmatocytes reveal gene specific signatures in response to clean injury and septic injury.

Drosophila melanogaster's blood cells (hemocytes) play essential roles in wound healing and are involved in clearing microbial infections. Here, we report the transcriptional changes of larval plasmatocytes after clean injury or infection with the Gram-negative bacterium Escherichia coli or the Gram-positive bacterium Staphylococcus aureus compared to hemocytes recovered from unchallenged larvae via RNA-Sequencing. This study reveals 676 differentially expressed genes (DEGs) in hemocytes from clean injury samples compared to unchallenged samples, and 235 and 184 DEGs in E. coli and S. aureus samples respectively compared to clean injury samples. The clean injury samples showed enriched DEGs for immunity, clotting, cytoskeleton, cell migration, hemocyte differentiation, and indicated a metabolic reprogramming to aerobic glycolysis, a well-defined metabolic adaptation observed in mammalian macrophages. Microbial infections trigger significant transcription of immune genes, with significant differences between the E. coli and S. aureus samples suggesting that hemocytes have the ability to engage various programs upon infection. Collectively, our data bring new insights on Drosophila hemocyte function and open the route to post-genomic functional analysis of the cellular immune response.

Introduction

Drosophila blood cells, also called hemocytes, contribute to the cellular immune response by engulfing bacteria, combatting parasites and secreting antimicrobial and clotting factors. They also participate in regulating the immune response by secreting cytokines such as the JAK-STAT ligands Unpaired [1] or the Toll ligand Spätzle [2-4]. Hemocytes are also involved in wound healing notably through the engulfment of apoptotic cells and cellular debris, the stimulation of stem cell proliferation, and deposition of extracellular matrix [5-8]. Furthermore, hemocytes produce enzymes essential to the melanization reaction [9,10]. Recent evidence shows that Drosophila blood cells contribute not only to immunity and wound healing, but are also central to host metabolism [11-14]. That an excessive number of hemocytes can be detrimental to flies raised on a poor diet shows that hemocyte number must be tightly regulated [15]. Thus, there is a current effort to better characterize the role of hemocytes during the life cycle of flies.

Hematopoiesis occurs in several waves throughout the Drosophila life cycle. The first phase of hematopoiesis establishes a pool of hemocytes from the embryonic head mesoderm. These cells contribute to embryonic development by phagocytosing apoptotic cells, and through the deposition of extracellular matrix [8]. These embryonic derived hemocytes persist in larvae, where they are subjected to several rounds of division reaching about 6000 hemocytes at the end of the third instar larval stage [16]. Peripheral larval hemocytes are found either (i) in circulation in the hemolymph or (ii) in sessile patches [17-23]. Sessile hemocytes are attached to the internal surface of the larval body wall, forming patches, some of which are closely associated with secretory cells called oenocytes, as well as the endings of peripheral neurons [22,24]. Hemocytes are continuously exchanged between sessile patches and circulation [25,26]. The function of sessile hemocyte patches is not yet established but it has been proposed that they form a diffuse hematopoietic organ [22,27,28]. Larvae also possess a special hematopoietic organ, the lymph gland, that functions as a reservoir releasing hemocytes at the pupal stage or after parasitic infection. Both lymph gland and embryonic derived hemocyte populations contribute to the pool of adult hemocytes that will ultimately decline upon ageing. Whether active hematopoiesis occurs in adults is still debated [29,30].

Most studies on the cellular immune response focus on third instar larval hemocytes as both sessile and circulating hemocytes can easily be collected and FACS sorted. Drosophila larvae have two types of hemocytes in the unchallenged state: plasmatocytes, which are macrophage-like, and crystal cells, rounded hemocytes which contain crystals of prophenoloxidases, the zymogen form of phenoloxidases that catalyzes the melanization reaction against parasites or septic injury [9,17,31]. A third type of hemocytes, the lamellocytes, is restricted to the larval stage and originates either from progenitors in the lymph gland or in periphery by transdifferentiation of plasmatocytes or circulating progenitors [32-34]. These cells differentiate upon parasitoid wasp infestation and contribute to the encapsulation and melanization of larger parasites. At the larval stage, plasmatocytes represent the most abundant fraction of Drosophila blood cells (i.e. 90–95%) [35] and express several markers such as the clotting factor Hemolectin (Hml), or the phagocytic receptors Nimrod C1 (NimC1 or P1) or Eater [18]. The other 5–10% larval hemocytes are Lozenge (Lz) positive crystal cells [17]. Only rarely can lamellocytes be observed in the unchallenged larvae as these cells are induced upon wasp infestation or injury [32].

Until recently, there have been surprisingly few studies analyzing the hemocyte transcriptome, possibly due to difficulties in collect enough materials. The most comprehensive genome wide analysis was a characterization of whole larval hemocyte populations by Irving et al. in 2005, using an Affymetrix based oligonucleotide array [2]. Of the 13,000 genes (total number of genes > 17,500) represented in this microarray, they were able to identify 2500 with significantly enriched expression in hemocytes, notably genes encoding integrins, peptidoglycan recognition proteins (PGRPs), scavenger receptors, lectins, cell adhesion molecules and serine proteases. Interestingly, several single cell transcriptomic analyses have revealed the degree of heterogeneity of Drosophila hemocyte populations, but they did not characterize the full repertoire of genes expressed in hemocytes [36-39].

To better characterize the transcriptome of hemocytes, we have performed an RNAseq transcriptome analysis of FACS sorted Hml positive cells. The transcriptome of Hml positive (Hml+) plasmatocytes was determined in an unchallenged condition and 45 minutes following clean or sceptic injury with Staphylococcus aureus or Escherichia coli. Comparative transcriptomics allowed us to identify a set of genes specific to plasmatocytes in unchallenged or challenged condition, revealing the various contributions of these cells to host defense, wound healing and metabolism.

Results

Study design

We performed RNA sequencing of mRNA to analyze the global gene expression profile changes of Drosophila hemocytes from third instar larvae either unchallenged or collected 45 minutes after clean injury or septic injury with a needle dipped in concentrated bacterial pellets of Staphylococcus aureus or Escherichia coli. To isolate the plasmatocytes from other unwanted cells of the hemolymph preparation, we used the HmlΔ.Ds-Red.nls fluorescent marker, which is specifically expressed in most plasmatocytes, and to a lesser extent in newly differentiated crystal cells [28,40,41]. We extracted hemolymph from wandering third instar L3 larvae by bleeding them onto a glass slide and subjected the collected hemolymph to fluorescence activated cell sorting (FACS) to isolate the Hml+ hemocyte population. The collected hemocytes thus correspond to circulating hemocytes. Flow cytometer scatter-plot outputs were analyzed to delineate the hemocyte population based on the nucleic red-fluorescent signal, and total RNA extraction was performed on the isolated hemocytes (). We collected approximately 20,000 to 40,000 larval Hml-positive plasmatocytes for each treatment. Three independent extractions were performed for each tested condition. As in Irving et al, 2005, we used unchallenged whole larvae as an external control to identify genes that were specifically enriched in plasmatocytes compared to the whole animal. RNA-Seq libraries were then constructed and sequenced using Illumina HiSeq, and we performed differential gene expression analysis between all sample groups. We obtained 62,320,223 reads from RNA samples extracted from whole larvae (L3), 42,284,187 reads from hemocytes of unchallenged larvae (UC), 42,519,937 reads from RNA extracted from hemocytes of clean-injured larvae (CI), 69,143,536 reads from RNA extracted from hemocytes of E. coli infected larvae (Ec) and 42,758,456 reads from RNA extracted from hemocytes of S. aureus infected larvae (Sa) (sum of triplicates). The total number of mapped reads per single library ranged from 5.30 to 23.17 million reads, with coverage ranging from 59.42% to 86.14% (). Genes with more than 5 reads per 1 million reads are listed in One of our unchallenged hemocyte samples showed elevated immune gene expression. To account for a possible unrelated infection, we reduced unwanted variation from this sample as described in the methods.

Experimental design of RNA sequencing experiment.

Total RNA was extracted from whole larvae or hemocytes recovered from HmlΔ.ds-red.nls fluorescent larvae. Larvae were left unchallenged or challenged by a systemic infection with Escherichia coli or Staphylococcus aureus and incubated at 29°C for 45 minutes. Hemocytes were collected in PBS, on ice, and were immediately sorted FACS and processed for RNA extraction.

Transcriptome summaries from unchallenged whole larvae and hemocytes from unchallenged and infected larvae.

(A) Transcriptome summary showing the number of reads for each triplicate in all experimental conditions with their corresponding number of mapped reads and the average percentage of alignment to the D. melanogaster genome. (B) Venn diagram representing the quantity of shared genes between all experimental treatments: Unchallenged wandering L3 larvae, hemocytes from unchallenged larvae, hemocytes from clean-pricked larvae (CI), hemocytes from larvae pricked with Escherichia coli (Ec), hemocytes from larvae pricked with Staphylococcus aureus (Sa).

We then identified differentially expressed genes (DEGs) between all five samples by four pairwise comparisons: “unchallenged hemocytes” samples versus “unchallenged whole larva” samples, “unchallenged hemocytes” samples vs hemocytes from clean injured larva (CIH), CIH vs hemocytes from “Escherichia coli infected larvae” (EcH) and CIH vs hemocytes from “Staphylococcus aureus infected larvae” (SaH).

Identification of genes enriched in Drosophila larval plasmatocytes

Using our threshold that includes genes with at least 5 reads per 1 million reads, we found that 6,723 genes are expressed in L3 larvae, while unchallenged hemocytes express 5,186 genes. The number of genes expressed in hemocytes is roughly the same as observed by Irving et al. using Affymetrix arrays that identified around 5000 expressed genes in hemocytes [2]. To identify transcripts expressed in the unchallenged hemocyte population, we classified genes according to their total number of reads () and their degree of enrichment in Hml+ plasmatocytes compared to whole larvae (, for a selection see ). We found that whole larvae and hemocytes shared expression of 4,477 genes, 2,246 genes were uniquely expressed in L3 larvae and 709 genes were uniquely expressed in hemocytes (). We confirmed the identity of the plasmatocyte population by the presence of reads for genes known to be specific for plasmatocytes (see below). We found 239 genes encoding transmembrane proteins in unchallenged hemocytes. Of those, 44 were enriched and 195 poorly expressed in hemocytes when compared to whole larvae (). This large set of transmembrane proteins likely contributes to the versatile functions of plasmatocytes. By secreting immune factors, the fat body plays a major role in the humoral response. Plasmatocytes are thought to play a similar role upon infection [42,43]. We therefore looked for genes encoding proteins with a secretion signal in plasmatocytes. We identified 329 such genes expressed in plasmatocytes. Among those, 70 were enriched and 259 were poorly expressed in plasmatocytes when compared to whole larvae ( and selection in ).

To better characterize gene repertoire of plasmatocytes, we will restrict our analysis to the 5393 genes that were differentially expressed in the whole larvae compared to plasmatocytes (). GO terms analysis () identified many biological processes without clearly highlighting important classes of genes. Thus, we decided to analyze in depth the DEGs identified in our initial analysis.

We first focused our attention on genes known to play a role in Drosophila hematopoiesis. We found that genes encoding the transcription factors Serpent [44], U-shaped [45] and Yantar [46], which play a role in pro-hemocyte differentiation, were enriched in plasmatocytes with respective fold changes (FCs) of 7.0, 6.6 and 2.7 compared to whole larvae. We did not identify glial cells missing (gcm) in our screen, which is consistent with the fact that this gene encodes a transcription factor promoting plasmatocytes maturation only at the embryonic stage [36,47]. The three genes dome, hedgehog and Antennapedia, which positively regulate hematopoiesis in the lymph gland [48], were reduced in circulating plasmatocytes compared to whole larvae, with FCs of -5.3, -173.9 and -2903.3, respectively. Similarly, genes that promote pro-hemocyte maturation in the lymph gland, such as jumu, pyramus, thisbe and heartless were also down-regulated in hemocyte samples (with FCs of -2.2, -31.9, -1151.8 and -1655.5, respectively) [49,50]. The gene encoding the transcription factor collier (knot) that contributes to the lymph gland posterior signaling center [51] was not enriched in plasmatocytes. In contrast, the two genes encoding the transcription factors Pointed and Pannier, which promote hemocyte terminal differentiation [50,52] were enriched in circulating hemocytes with FCs of 3.5 and 15.6 respectively. Finally, genes implicated in crystal cell differentiation such as Delta, serrate and notch [48] were downregulated in plasmatocytes samples compared to the whole larvae samples, with respective FCs of -919.3, -9.9 and -4.5. In contrast, the expression of lozenge gene, which encodes the master regulator of crystal cell differentiation, was not affected. The expression of lozenge in Hml+ plasmatocytes possibly reflects the trans-differentiation of a subset of them into crystal cells [28]. These results confirmed that collected circulating peripheral plasmatocytes were mostly in the differentiated state. Consistent with this, the Drosophila hemocyte marker genes hemese, peroxidasin and hemolectin had respectively 49.0, 15.5 and 6.7 fold higher expression in hemocytes compared to whole larvae samples.

As expected, plasmatocytes were strongly enriched in genes involved in phagocytosis. We found the scavenger receptor class C, type I (ScrCI), and the Nimrod receptors Nimrod C1 [53] and eater [54] had respective FCs of 48.9, 26.5 and 15.3. We did not identify the Integrin βν subunit to be differentially expressed in our screen. Two other Nimrod receptors, draper and simu (Six-Microns-Under, also named Nimrod C4) can bind phosphatidylserine on dying cells and promote apoptotic cell internalization, a process called efferocytosis [55,56]. In our screen, we found draper transcripts enriched in plasmatocytes samples with a FC of 3.1 whereas simu expression was unchanged. Hml+ plasmatocytes were also enriched in genes encoding opsonins such as Tep1 (FC: 19.2) which has been shown to promote bacterial internalization [57,58]. In contrast, Tep2 and Tep3 were down-regulated in hemocytes compared to the whole larvae samples (FCs: -8.4 and -63.1). Furthermore, several genes encoding secreted components of the Nimrod family were enriched in plasmatocytes, most notably Nimrod B1 and Nimrod B4 and to a lesser extent Nimrod B5 (FCs: 23.2, 17.6 and 3.7) as well as hemese. Genes encoding secreted Nimrod genes and hemese are clustered in the genome together with genes coding for phagocytic receptors such as Nimrod C1. Recently, NimB5 has been shown to regulate plasmatocyte adhesion and proliferation [15]. The two plasmatocyte-enriched secreted Nimrod proteins, NimB1 and NimB4, are promising candidate genes regulating important plasmatocyte functions, possibly phagocytosis [59]. Drosophila plasmatocytes were also enriched in several cytoskeleton proteins (see in ) such as SCAR, which has been shown contribute to phagocytosis and cell migration [60]. The two GTPases, Rac1 and Rac2, which have been implicated in phagocytosis and cellular response were also enriched in plasmatocytes (FCs: 2.8 and 4.1) [61-63]. A recent study revealed an important role of peroxisomes in phagocytosis and immunity [64]. Consistent with this, several peroxins that encode components of peroxisomes (peroxins 19, 12, 2, 11, 10, 16, and 14) were strongly enriched in plasmatocytes. In addition, several genes encoding Tetraspanins were enriched in plasmatocytes. Tetraspanins are implicated in a wide range of functions in Drosophila such as protein stabilization at the plasma membrane and cell signaling regulation, and could contribute to phagocytosis or adhesion [65]. Specifically, we identified late bloomer and the Tetraspanins 86D, 42Ee, 3A and 96F with respective FCs ranging from 2.8 to 3.9. Collectively, the enrichment of genes encoding phagocytic receptors, opsonins, and cytoskeletal proteins in plasmatocytes confirm their phagocytic ability.

The systemic antimicrobial response, which encompasses the production and release of many immune effectors into the hemolymph, is regulated by two NF-κB pathways, namely Imd and Toll [66]. There is strong evidence that these two pathways are functional in hemocytes [67]. We found that plasmatocytes are enriched in several components of the Imd pathway, notably Imd and Relish. The genes encoding three transmembrane receptors of the Peptidoglycan recognition proteins (PGRP) family, PGRP-LF, PGRP-LC, and PGRP-LA, which are organized in a cluster in the Drosophila genome and contribute to Imd pathway activity, were also higher in plasmatocytes (FCs: 3.4, 3.2 and 2.2 respectively) [68,69]. The gene encoding the intracellular pattern recognition PGRP-LE that is involved in the sensing of monomeric peptidoglycan of Gram-negative bacteria and autophagy was also increased in plasmatocytes (FC: 2.7) [70-72]. We also confirmed that plasmatocytes have an increased expression of the gene spatzle (FC: 10.5), which encodes the ligand of the Toll pathway [66,73] as well as the genes encoding the adaptor Tube and the kinase Pelle (respective FCs: 3.1 and 5.3).

Crystal cells are the main hemocyte type involved in the melanization reaction, expressing both Prophenoloxidases 1 (PPO1) and 2 (PPO2) [9]. In contrast, Prophenoloxidase 3 (PPO3) is expressed in lamellocytes [10]. Surprisingly, we found that both PPO2 and PPO3 were enriched in circulating cells with fold changes of 9.1 and 25.5 compared to whole larvae. Their expression in plasmatocytes could reflect the transdifferentiation of Hml positive cells into crystal cells and lamellocytes or an unexpected contribution of plasmatocytes in the production of prophenoloxidase. Plasmatocytes had increased expression of genes encoding for several of the enzymes that have been linked to prophenoloxidase activity (e.g. yellow f, FC: 13.55; see ). Complex serine protease cascades regulate important immune functions (i.e. melanization, Toll) in the hemolymph. We found that plasmatocytes have higher expression of the gene encoding the serine protease MP1 (FC: 11.2), which regulates melanization, and serpins such as Serpin27A (FC: 7.9) and Serpin28Dc (FC: 3.2), which negatively regulate melanization and Toll [74-77]. The immune function of lectins is poorly characterized in Drosophila but some of them have been implicated in immunity in other insects [2,78,79]. We found that two secreted lectins, lectin-24Db and lectin-28C were strongly enriched in plasmatocytes (FCs: 33.9 and 24.4).

Plasmatocytes also showed increased expression of many genes involved in the oxidative stress response, notably many glutathione S transferase genes and other detoxifying enzymes such as peroxidasin (FC 15.51), superoxide dismutases 1 and 2 (FCs: 2.8 and 3.5) or thioredoxin peroxidase 1 and 2 (FC: 2 and 2.4). These genes may play a role in immune response activation [80]. In parallel, we observed an increase in gene transcripts involved in other cellular stress pathways, e.g. many heat shock proteins and Ninjurin B (FC: 5.9). We cannot exclude the possibility that this expression profile related to cellular stress also reflects a quick response of plasmatocytes to stresses imposed by procedures (i.e. temperature switch from 25°C to 29°C and FACS processing), despite maintaining samples on ice following extraction.

Plasmatocytes contribute to the production of the basal membrane in the embryo [81]. In agreement with this, larval plasmatocytes had increased expression of genes encoding components of the basal membrane, notably Laminins A, B1 and B2 (FCs: 5.7, 7.2 and 8.7) [8], Viking (collagen IV), and secreted enzymes that contribute to basal membrane formation (fat-spondin, Glutactin, Peroxidasin, Matrix metalloproteinase 2 with FCs of 19.1, 18.6, 15.5 and 4.9). Consistent with previous studies that have suggested an important role of plasmatocytes in adenosine metabolism at the larval stage [82] we found that the two genes Adenosine deaminase-related growth factor A (ADGF-A) and Adenosine deaminase were upregulated (FCs: 6.59 and 5.59) while Adenosine deaminase-related growth factor A2 and D were downregulated. Recent studies have also highlighted a significant contribution of plasmatocytes in lipid uptake and storage, complementary to the fat body [83]. Consistent with this, the gene croquemort, which encodes a lipid binding receptor of the CD36 family, is also enriched in plasmatocytes (FC: 4.7) [84]. The fatty acid binding protein (fabp) gene was enriched 9-fold in plasmatocytes. Finally, our transcriptome analysis shows that plasmatocytes express many transporters, the aquaporin Prip being one of the most enriched compared to other tissues (FC: 12.4).

The early plasmatocyte response to clean injury

We then explored the transcriptomic response of plasmatocytes to clean injury in further detail by comparing the transcriptome of hemocytes extracted from unchallenged larvae to hemocytes from larvae 45 minutes after clean injury. We choose this time point as it corresponds to the time necessary to fully internalize or phagocytose a particle [85]. We identified 664 DEGs after clean injury compared to hemocytes from unchallenged larvae. Among these genes, 358 were up-regulated and 306 were down-regulated in the clean injury samples using a two-fold change criteria and p<0.05 cut-off ( See selection in ). We then proceeded to GO terms analysis by focusing on genes that are upregulated in hemocytes after clean injury compared to unchallenged hemocytes, and that have a P-value < 0.05. We identified several GO-term groups significantly enriched upon clean injury. Genes with GO terms for molecular function assigned to cell metabolism, actin mobilization and cytoskeleton organization, anti-oxidant and stress responses were particularly affected ().

We found a significant increase in transcripts corresponding to Glutathione-S-transferase (GST) genes (D2, E1, D3, E3, E8, D7, D5, E6) which encode antioxidants enzymes that detoxify hydrogen peroxide and lipid peroxides [86,87]. Genes encoding three Cytochrome P450 enzymes (Cyp6a20, Cyp6a17 and Cyp6a23) and the ABC transporter Multidrug resistance protein 4 (Mrp4), which are involved in detoxification, were also enriched upon clean injury (FC: 10.5, 3.1, 2.8 and 2.8).

Other stress responsive genes such as Frost, Hsp70, Hsp68, Ninjurin A, the Hsp co-factor starvin and DnaJ-like-1 were also more highly expressed in clean injury conditions. Consistent with this stress response, the JNK stress responsive pathway was activated as evidenced by an increase expression of puckered (puc, FC: 2.3).

Many other upregulated genes such as Gadd45, kayak, Larval cuticle proteins 1, 3 and 4, and Matrix metalloproteinase 1 which play a key role in wound healing, extracellular matrix generation and cuticle repairing, were also more highly expressed in plasmatocytes [88,89].

Our study confirms that the JAK-SAT ligand gene upd3 which orchestrates the systemic wound response [3,90] was upregulated in plasmatocytes upon clean injury (FC: 4.2). The gene encoding Wallenda, a MAP3K that regulates stress response by regulating the expression of the Materazzi lipid binding protein gene in Malpighian tubules [91], was also upregulated (FC: 2.6).

It is well established that clean injury, in the non-sterile condition used in this study, triggers a transient and weak antimicrobial response [92]. We found that genes encoding components of the immune responsive pathways Imd (Relish, PGRP-LB, PGRP-LF) and the Toll pathways (cactus) were upregulated (respective FCs: 4.5, 3.2, 2.4 and 2.1). A subset of antimicrobial peptide genes, notably Cecropin B, A2 and C (respective FCs: 13, 8.3 and 6.8) and Attacin-B (FC: 6.8), were upregulated by clean injury.

Interestingly, two genes whose mutations have been associated with refractoriness to virus C and Sigma, pastrel and ref(2)P respectively, were upregulated upon clean injury (FCs: 3.1 and 2.0) [93]. While the function of ref(2)P in autophagy is well established [94], the role of pastrel is poorly characterized. The induction of pastrel in plasmatocytes suggests that it could play an important function in activated plasmatocytes, as the result of a potential viral infection.

Genes such as fondue (FC: 4.2), Larval-serum protein 1γ (FC: 8.3) and Hemolectin (FC: 2.2) that are implicated in hemolymph clotting [95] were up-regulated in clean-injured samples. Of note, dopa decarboxylase and the GTP cyclohydrolase punch, genes which encode enzymes that regulate melanin formation, were enriched in clean injury samples confirming the contribution of plasmatocytes to the melanization process [96]. Collectively, our study shows that plasmatocytes contribute to wound healing by inducing genes involved in stress response, ROS detoxification and cytoskeletal remodeling. The induction of genes encoding components of the Toll and Imd immune signaling pathways may reinforce the reactivity of these immune cells.

Another major class of genes upregulated upon clean injury are those involved in the cytoskeleton (). This includes genes involved in actin remodeling, microtubule formation and adhesion that likely reflect the change of shape observed in ‘activated plasmatocytes’ that are known to be more adhesive and display filopodia [97]. Among them, were the Integrin alphaPS5 subunit gene (FC: 7.1) [98] and the integrins charlatan and myospheroid (FCs: 7.4 and 2.1) [99,100] which are induced upon lamellocyte differentiation. Interestingly, Integrin βν subunit, a gene that encodes a transmembrane protein implicated in apoptotic corpses clearance in embryonic hemocytes [101], and Scavenger receptor class C, type III (Sr-CIII) were also induced (FCs: 3.9 and 2.7). Moreover, the gene encoding the FGF ligand Pyramus that has been shown to promote blood cell progenitors differentiation in the lymph gland [50] was the most up-regulated gene 45 minutes after clean injury. This suggests that FGF-R pathway activation in plasmatocytes by the ligand Pyramus could play a prominent role in promoting the differentiation of peripheral plasmatocytes upon injury, akin to the process observed in the lymph gland. On this line, Tattikota et al. observed an enrichment of transcripts encoding the FGF ligand Branchless and its receptor Breathless in crystal cells and lamellocytes subsets respectively, and revealed a role of the FGF pathway in the encapsulation of parasitoid wasps [37]. This also highlights the importance of the FGF pathway in cell specification. Two genes from the PVR (PDGF/VEGF-related factor) pathway that are implicated in hemocyte survival and migration [102-104], one encoding the PVR adaptor PVRAP and the other the PVR ligand Pvf2, were up-regulated upon clean injury (FCs: 2.5 and 3.8). Consistent with this, we also observed that clean injury triggers the down-regulation of apoptosis-associated genes such as head involution defective (hid, FC: 15.7) [105] and Deneddylase 1 (Den1, FC: 2.2) [106]. This suggests that wounding stimulates blood cell survival as well as blood cell pool expansion.

In mammals, macrophages undergo massive metabolic change upon activation [107,108]. Notably, lipid catabolism and glucose consumption are essential components of mammalian macrophage activation in order to fuel the cell as well as to produce inflammatory mediators [109]. We next investigated whether clean injury also induces a metabolic reprogramming in plasmatocytes. Interestingly, when analyzing the GO terms enrichment, we found an over-representation of “lipase activity” related genes (). Indeed, we observed the up-regulation of the magro, alpha/beta hydrolase2 (Hydr2), the phospholipase c at 21c and no receptor potential A (norpA) genes with respective FCs of 26.4, 2.9, 2.6 and 2.2. Upregulation of apolipophorin (apoLpp) and ATP binding cassette subfamily A (ABCA) genes (FCs: 2 and 4.2) indicate that both lipid catabolism and lipid uptake are induced upon clean injury in plasmatocytes, which may fuel the increased energy demand of the activated cells. In agreement with the concept of metabolic reprogramming, we noted the up-regulation of the Glucose transporter 4 enhancer factor gene (Glut4EF, FC 8.9) [110], a transcription factor regulating the Glucose transporter 4 gene [111], also known as solute carrier family 2 member 4), and of the Glycogen phosphorylase (GlyP) gene coding for the enzyme catalyzing the rate-limiting step of glycogenolysis. The induction of these two genes suggests that upon injury plasmatocytes may increase their metabolic activity by increasing glucose provisioning. Genes of the mTOR signaling pathway, that is known to stimulate a glycolytic metabolism, were also upregulated: thor, rictor and phosphoinositide-dependent kinase 1 (pdk1) and (FCs: 2.7, 2.5 and 2.0). Additionally, the men gene encoding malate deshydrogenase, which is known to sustain active glycolysis by replenishing the cytosolic NAD pool and by limiting tricarboxylic acid cycle (TCA) refueling [112], was also upregulated.

Plasmatocytes gene expression signature in response to bacterial infection

Finally, we explored the transcriptional response of blood cells upon septic injury with Escherichia coli (EcH samples, and ) and Staphylococcus aureus (SaH samples, and ) and compared it with the transcriptional profile of hemocytes from clean-injured larvae. We were interested to know if the presence of bacteria affects plasmatocyte response and whether hemocytes react in a different way to infection by Gram-negative versus Gram-positive bacteria (). Studies have shown that E. coli is an efficient inducer of the Imd pathway and is sensitive to the action of antibacterial peptides [113]. In contrast, S. aureus, as a lysine-type bacterium, is a potent inducer of the Toll pathway [114] and is combatted by Toll mediated production of Bomanin [113], melanization [77], and phagocytosis [115]. Interestingly, we identified 104 and 92 uniquely expressed genes in hemocytes from larvae infected with E. coli (EcH) and S. aureus (SaH) respectively compared to hemocytes from clean injured larvae. In EcH samples, we identified 84 up-regulated genes and 151 down-regulated genes whereas in SaH samples, we identified 103 up-regulated genes and 81 down-regulated genes (see and ). The two significantly enriched GO component categories upon infection with E. coli or S. aureus correspond to secreted components (GO term GO:0005615 and GO:0044421) and an “antibacterial humoral response” (GO:0019731) which is consistent with an increased expression of antimicrobial peptides (AMP) upon infection compared to clean injury (, and , see selections in Tables and ).

Genes induced in hemocytes upon bacterial infection.

Venn diagram illustrating genes that were enriched in hemocytes from larvae pricked with Escherichia coli (E. coli) or Staphylococcus aureus (S. aureus) compared to clean-pricked larvae (CI).

Our RNAseq study reveals a small subset of genes that were induced upon both E. coli and S. aureus, notably the antimicrobial peptide coding gene Metchnikowin (Mtk, against Gram-positive and fungi, FC 7.3) and surprisingly, many genes annotated as 18S or 28S ribosomal RNA pseudogenes. Challenge with E. coli leads to specific induction of several Imd target genes, notably the antibacterial peptides Diptericin B, Cecropins A1, A2 and C, Attacins A and D, as well as PGRP-LB, a gene encoding a negative regulator of the Imd pathway that scavenges peptidoglycan [116]. Another immunity gene, edin, was also upregulated in EcH samples compared to clean injury samples (FC: 3.1). Edin has previously been described as upregulated in S2 cells upon E. coli infection and is needed for the increase in plasmatocyte numbers and for the release of sessile hemocytes into the hemolymph upon wasp infection [117]. Thus, the increase in edin could reflect the mobilization of sessile hemocytes into circulation. It is important to note the down-regulation of several heat-shock protein genes such as Hsp27, Hsp70ab, and Hsp70Bc in the EcH samples (FCs: -2.0, -3.0 and -3.0). Thus, septic injury with E. coli tends to orient the hemocyte towards an antibacterial response while clean injury directs a stress and repair response.

As in EcH samples, SaH samples also show an enrichment of GO processes associated with the immune response, such as the up-regulation of the Metchnikowin gene but also Diptericin B (FC: 3.57 and 2.19) and one Bomanin gene: Bomanin Short 3 (BomS3, FC: 2.0). The specific induction of antibacterial peptide genes (AttD, Cec), known to be regulated by the Imd pathway in response to E. coli but not S. aureus indicates that the hemocytes can mount a differentiated response to these two bacteria within 45 minutes. We also observed an increase in expression of Growth-blocking peptide 1 (Gbp1). Gbp1 was characterized as a cytokine that plays a role in IMD activation upon Gram-negative bacterial challenge in both the fat body and hemocytes [118]. Our study rather points to a specific induction of Gbp1 in response to a Gram-positive bacterial challenge. Several genes encoding proteins that could function in phagocytosis such as Biogenesis of lysosome-related organelles complex 1, subunit 2, and Tetraspanin 42El were specifically enriched by two-fold in S. aureus versus clean injury. Several genes implicated in cell division were down-regulated in S. aureus samples compared to clean injury samples, such as mitotic spindle and nuclear protein (mink, -2.4 fold change) [119], stathmin (stai, -3.8 fold change) [120] and cyclin B (cycB, -2.2 fold change) [121] suggesting a cell cycle arrest in response to infection with this Gram-positive bacterium () [122]. Surprisingly, the gene encoding the lipase Magro was expressed 14 times less upon systemic infection with S. aureus compared to clean injury or EcH. Thus, at the 45 minutes time point, a challenge by S. aureus tends to orient plasmatocytes towards a lower production of secreted factors and decreased lipid catabolism. We hypothesize that plasmatocytes contribute to host defense in different ways against S. aureus and E. coli. Phagocytosis of S. aureus may not be compatible with cell division. Decreased lipase activity may reflect a reduced energy demand of these plasmatocytes compared to energetically expensive AMP production in those infected with E. coli.

Discussion

The Drosophila immune response has been the focus of extensive genome-wide gene expression studies that open the route to successful post-genomic functional characterization of novel immune genes [123]. In contrast, transcriptome studies of hemocytes have been rather limited, or have used S2 or mbn-2 hemocyte-derived cell lines that do not reflect an integrated model [2,124,125]. This was mostly due to the difficulties in collecting enough pure material, as hemocytes represent a tiny fraction of Drosophila larvae. Recently, FACS sorting of hemocytes, and the use of new hemocyte-specific markers has facilitated the extraction of plasmatocytes. Taking advantage of this, we performed a comprehensive RNAseq analysis of Hml+ plasmatocytes in absence of infection and 45 minutes following clean injury or septic injury with E. coli or S. aureus. We found that FACS purification did not affect the lineage characteristics of the sample allowing us to use these very pure populations to characterize transcriptomic variation in Hml+ positive plasmatocytes. As Hml+ cells are widely used to study hemocyte function such as adhesion, sessility, metabolism and phagocytosis, our dataset is an important contribution to the community. Our RNAseq study also complements a recent single cell analysis of Drosophila hemocytes that has revealed the various states of hemocyte differentiation [36,37,38,39]. It is important to note that our chosen experimental design may have affected the transcriptome of unchallenged hemocytes slightly. We incubated all larvae, previously reared at 25°C, for 45 minutes at 29°C after treatment to accelerate the transcriptional response, which might result in a small heat shock induction. Despite this limitation, comparisons between conditions revealed interesting patterns of expression that could be useful to further functional studies of hemocytes.

Our study provides the full repertoire of genes expressed in plasmatocytes and their expression levels, notably those encoding transmembrane proteins and secreted factors ( and ). Screening these genes in future studies will allow a better characterization of plasmatocyte functions in phagocytosis, migration, and sessility and a greater understanding of how these motile cells interact with other tissues. Consistent with a previous Affymetrix based study [2] and a recent single cell study [36], we found that plasmatocytes express a large repertoire of phagocytic receptors, opsonins and cytoskeleton proteins, reflecting their important function as phagocytes. Consistent with older studies that demonstrated that hemocytes produce antimicrobial peptides upon challenge [30,42,126], we confirmed that hemocytes express genes encoding several components of the Toll and Imd pathways, as well as some of their downstream target genes. They have the ability to induce a subset of antimicrobial peptides upon challenge. Use of AMP-reporter genes [42,126] and recent single cell analysis [36] has shown that only a fraction of plasmatocytes express antibacterial peptides upon immune challenge, indicating that some sub-populations of plasmatocytes are specialized for this task. One of the most surprising observations is the high expression of Attacin-D in plasmatocytes, as this antibacterial peptide is devoid of a signal peptide. The precise role of this AMP is unknown, and further studies should decipher whether it is secreted by an atypical mechanism or if it functions intracellularly. Our study confirms that plasmatocytes contribute to clotting and melanization, two important hemolymph immune functions [127]. Surprisingly, our RNAseq analysis detected an unexpected expression of PPO2 and PPO3 in Hml+ plasmatocytes, which were previously shown to be specific to crystal cells and lamellocytes. As single cell analysis studies have confirmed that PPO2 and PPO3 are indeed restricted to crystal cells and lamellocytes [36], the high expression of PPO2 and PPO3 in our sample can be explained by the presence of Hml+ plasmatocytes that are undergoing their transdifferentiation into crystal cells and lamellocytes [27,28,99]. The fat body, particularly at the larval stage, is the main organ producing hemolymphatic proteins. Our study confirms that like the fat body, Hml+ plasmatocytes also express a large repertoire of secreted proteins, notably components of the extracellular matrix and opsonins. It is currently unclear how the synthesis of hemolymphatic protein is allocated between the fat body and plasmatocytes, or why plasmatocytes are involved in the secretion of molecules such as extracellular matrix components or AMPs. An interesting hypothesis is that plasmatocytes, by virtue of their migratory ability, function as local repairers that can locally supply and enrich specific factors. Indeed, a recent study indicated that a distinct pool of plasmatocytes, the "companion plasmatocytes" expressing collagen IV, are tightly associated with the developing ovaries from larval stages and onward [128]. Eliminating these companion plasmatocytes or specifically blocking their collagen IV expression during larval stages causes abnormal ovarian niches with excess stem cells in adults. This suggests that hemocytes could have short-range action consistent with the notion of local repairers. Deciphering specific roles for the hemocytes and fat body in the production of hemolymphatic proteins is an interesting prospect.

Despite extensive research on Drosophila melanogaster blood cell adaptation to wounding [33,129] only rare transcriptomic analyses has been performed, and have specifically analyzed their response to clean injury. One main study performed in 2008 tried to provide insight into specific hemocyte response to wounding. Stramer et al. compared transcriptional responses of wild-type embryos and serpent null embryos, which are devoid of hemocytes, 45 minutes following wounding. They identified a limited number of significantly affected genes, probably because of a dilution effect due to the limited number of hemocytes overall in embryos [88]. However, they identified the up-regulation of secreted phospholipase A2 that plays a role in the production of eicosanoids, key signaling molecules that limit inflammation [130]. In our study, we observed that the gene encoding the calcium-independent phospholipase A2 VIA was strongly induced upon wounding. This phospholipase has been shown in mammals to promote adhesion, clearance of debris and ROS production to act as a chemoattractant [131]. In contrast to the work done by Stramer et al., we did not find any induction of Drosomycin in the wounding condition, likely due to the short time point we used. Morphological studies have shown that hemocytes modify their shape, change their adhesive properties and start to transdifferentiate into plasmatocytes and lamellocytes upon clean injury. A recent single cell analysis has deciphered some of the changes that take place in hemocyte populations 24 hours after clean injury, reflecting a change in their differentiated states [37]. Our study reveals that major transcriptome changes have already begun 45 minutes post-challenge. These changes likely reflect the transformation of resting plasmatocytes into an activated form, and their differentiation into more specialized hemocyte sub-types. The observation that the FGF ligand Pyramus that mediates blood cell progenitors differentiation in the lymph gland [50] was the most up-regulated gene 45 minutes after clean injury suggests that FGF-R pathway activation could play a prominent role in promoting the differentiation of peripheral plasmatocytes upon injury. In this work, we also show that wounding reduces apoptotic processes in blood cells while promoting cell proliferation, consistent with a previous study showing that wounding stimulates de-novo peripheral blood cell proliferation [132]. In accordance with these data, we observed the up-regulation of pvf2 [102] and the PVR adaptor [133] upon clean injury. Interestingly, the PVF pathway also plays a role in hemocyte migration, as Pvf2 acts as a chemoattractant [104]. It is possible that hemocytes in close contact to the wounding site stimulate nearby hemocytes to improve wound healing and accelerate repair processes. Our results also show that plasmatocytes, like mammalian macrophages, undergo major metabolic reprogramming following injury that likely fuels their transformation into an ‘activated’ plasmatocyte state that is more effective at producing secreted factors or engulfing bacteria. Future studies should better characterize how immune functions are coupled with metabolic reprogramming in hemocytes.

Finally, our study reveals that hemocytes can mount specific responses to different pathogens such as E. coli and S. aureus. The early time point we chose likely prevented us from fully capturing this differentiation. The polarization of T helper cells into sub-categories in response to different cytokine environments is well established. This concept has recently been extended to innate immune cells in mammals, notably to macrophages with their M1 pro-inflammatory and M2 pro repair subtypes [134]. It is tempting to speculate that Drosophila plasmatocytes can be polarized toward different functions such as enhanced production of antibacterial peptides or phagocytosis according to different inflammation and metabolic states. This would explain the existence of various plasmatocyte populations in different activity states [36,37,38,39]. In this vein, it would be interesting to further characterize the transcriptome of plasmatocytes in response to other challenges such as phagocytosis of apoptotic cells and yeast. Another interesting prospect is to decipher whether sessile and circulating plasmatocytes differ in their transcriptional activities. Collectively, our study and a recent single cell analysis underline the complexities of the cellular response and open the route to functional analysis.

Material and methods

Drosophila stocks and rearing

In this work, we used w;;Hml.DsRed.nls line. Animals were reared on standard fly medium comprising 6% cornmeal, 6% yeast, 0.62% agar, and 0.1% fruit juice, supplemented with 10.6g/L moldex and 4.9ml/L propionic acid. Flies are maintained at 25°C on a 12 h light/ 12 h dark cycle. Both males and females were used for experiments.

Microorganism culture and infection experiments

The bacterial strains used and the respective optical density (O.D.) of the pellet at 600 nm were: Staphylococcus aureus (O.D. 0.5) and Escherichia coli (O.D. 0.5). L3 wandering larvae were pricked with a tungsten needle on the dorsal side, at the origin of the two trachea, corresponding to the A7 or A8 segments. Pricked larvae were placed into a small petri dish with fresh medium and incubated at 29°C for 45 minutes. We then dissected larvae on a glass slide in a 120 ul PBS droplet before cell sorting.

Cell sorting procedure

Cell sorting was performed on a BD FACS Aria II (Becton Dickinson, San Jose, CA, USA) fitted with a 100 μm nozzle and with pressure set at 20 PSI. The machine temperature was lowered at 4°C, samples were recovered in Eppendorf tubes kept on ice and immediately resuspended in TRIzol™ (#15596018, Thermo Fisher Scientific, Waltham, MA, USA). Hemocytes were selected and sorted based on DsRed fluorescence.

RNA extraction, sequencing and analysis

For whole larva RNA extraction, 20 animals were homogenized in tubes with glass beads and lysed with the use of a PRECELLYS™ homogenizer, with 0.5 mL of TRIzol™ reagent and 0.3 mL of chloroform. For recovery of hemocytes during FACS procedure, cells were directly resuspended in the same mix of TRIzol™-chloroform. RNA was extracted following the classical phenol-chloroform RNA extraction technique.

For all samples, RNA quality was assessed on a Fragment Analyzer (Agilent Technologies, Inc., Santa Clara, CA 95051, USA). RNA-seq libraries were prepared using 73–100 ng of total RNA and the Illumina TruSeq Stranded mRNA reagents (Illumina; San Diego, California, USA) according to the supplier’s instructions. Cluster generation was performed with the resulting libraries using the Illumina TruSeq SR Cluster Kit v4 reagents and sequenced on the Illumina HiSeq 2500 using TruSeq SBS Kit v4 reagents. Sequencing data were demultiplexed using the bcl2fastq Conversion Software (v. 2.20, Illumina; San Diego, California, USA).

The quality of the resulting reads was assessed with ShortRead (v. 1.28.0) [135]. Reads were then aligned to the reference genome (Drosophila_melanogaster BDGP6 dna.toplevel.fa) with TopHat (v2.1.0) and Bowtie (2.2.6.0). Mapping over exon-exon junctions was permitted by supplementing annotations (Drosophila_melanogaster BDGP6.87 GTF). The reads acquired in this way were used to create the lists of expressed genes (cutoff 5 counts per million [CPM] in the average of all triplicates) for each respective treatment. Unwanted variation from this data was removed by using RUVSeq (3.10) by estimating the factors of unwanted variation using residuals [136]. Differential expression analysis was performed with edgeR (3.26.4) [137].

Gene Ontology (GO) analysis was performed with Gorilla (online version http://cbl-gorilla.cs.technion.ac.il/ - July and August 2019). Two unranked lists of genes were compared, where the background set of genes was all genes with expressed with minimum of 5 CPM reads from all three combined unchallenged hemocyte reads. The target set of genes was determined by the results of the differential expression analysis of the respective treatment with the following cutoffs: CPM > 5, P-value < 0.05, FC > +/-1.88. Fold changes are expressed as real values and Log2 based values.

GO terms enriched in unchallenged hemocytes compared to whole larva.

(TIF)

Click here for additional data file.

GO terms enriched in clean injury hemocytes compared to unchallenged hemocytes.

(TIF)

Click here for additional data file.

Selected DEGs of interest linked to extracellular matrix reorganization, extracted from the comparison of clean injury hemocytes vs. unchallenged hemocytes.

(DOCX)

Click here for additional data file.

Raw reads numbers in all samples (L3, UC, CI, E coli and S aureus).

Average of the number of mapped reads per million reads in the respective triplicate samples. GO terms were extracted from Flybase.

(XLSX)

Click here for additional data file.

Differentially expressed genes in unchallenged hemocytes versus unchallenged whole larvae samples.

Results of the differential gene expression analysis between unchallenged plasmatocytes and L3 whole larvae. Cut-off values are fold change ≥ 2, logCPM > 2, P-value < 0.05.

(XLSX)

Click here for additional data file.

Transmembrane proteins coding genes identified in S2 File.

Differentially expressed genes between unchallenged hemocytes and L3 whole larvae which have the GO term ‘integral component of plasma membrane’ or ‘cell surface’. Cut-off values are fold changes ≥ 2, logCPM > 2, P-value < 0.05. Positive FC values indicate higher expression in plasmatocytes versus L3.

(XLSX)

Click here for additional data file.

Secreted proteins coding genes identified in S2 File.

Differentially expressed genes between L3 whole larvae and unchallenged hemocytes that have the GO term ‘extracellular space’ or ‘extracellular region’, which identifies putative secreted proteins regardless of the presence of a signal peptide. Cut-off values are fold changes ≥ 2, logCPM > 2, P-value < 0.05. Positive FC values indicate higher expression in plasmatocytes versus L3.

(XLSX)

Click here for additional data file.

Differentially expressed genes in clean injury samples versus unchallenged samples.

Results of the differential gene expression analysis between unchallenged hemocytes and hemocytes from animals with clean injury. Cut-off values are fold changes ≥ 2, logCPM > 2.3, P-value < 0.05. Positive FC values indicate higher expression in clean injury samples versus unchallenged samples.

(XLSX)

Click here for additional data file.

Differentially expressed genes in E. coli samples versus clean injury samples.

Results of the differential gene expression analysis between hemocytes from animals infected with E. coli and hemocytes from animals with clean injury. Cut-off values are fold changes ≥ 2, logCPM > 2.3, P-value < 0.05. Positive FC values indicate higher expression in E. coli samples versus clean injury samples.

(XLSX)

Click here for additional data file.

Differentially expressed genes in S. aureus samples versus clean injury samples.

Results of the differential gene expression analysis between hemocytes from animals infected with S. aureus and hemocytes from animals with clean injury. Cutoff values are fold changes ≥ 2, logCPM > 2.3, P-value < 0.05. Positive FC values indicate higher expression in S. aureus samples versus clean injury samples.

(XLSX)

Click here for additional data file.

21 May 2020

PONE-D-20-12312

Comparative RNA-Seq Analyses of Drosophila Plasmatocytes Reveal Gene Specific Signatures In Response To Clean Injury And Septic Injury

PLOS ONE

Dear Bruno,

Thank you for submitting your manuscript to PLOS ONE. It has now been evaluated by three expert reviewers. They all liked the work, but suggested minor revisions to further improve it. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised by the reviewers in their reviews.

The reviewers had trouble accessing all supplemental figures. When I tried, I was only able to download three of the seven supplemental figures. I don't know the cause of this (could be a glitch in the Plos One editorial manager). Please make sure when uploading the revision that all figures upload successfully.

We would appreciate receiving your revised manuscript within two months. If you need more time, please let us know. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Andreas

***

Andreas Bergmann, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you are reporting an analysis of a microarray, next-generation sequencing, or deep sequencing data set. PLOS requires that authors comply with field-specific standards for preparation, recording, and deposition of data in repositories appropriate to their field. Please upload these data to a stable, public repository (such as ArrayExpress, Gene Expression Omnibus (GEO), DNA Data Bank of Japan (DDBJ), NCBI GenBank, NCBI Sequence Read Archive, or EMBL Nucleotide Sequence Database (ENA)). In your revised cover letter, please provide the relevant accession numbers that may be used to access these data. For a full list of recommended repositories, see  or .

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The experimental set-up is simple, straight forward and strategic. All possible combinations were investigated and the findings well discussed in the light of the literature. The datasets will provide a basis for further studies on possible differential interaction among hemocyte (plasmatocyte) subsets in the immune response to S. aureus and E. coli.

The studies comprehensive describe the functions of the differentially expressed genes and activation networks.

The figures are of good quality and informative.

The differences in gene expression pattern in the response to S. aureus and E. coli could be elaborated a bit more. Maybe a figure (direct comparison) could help to make the findings and conclusions more transparent.

Reviewer #2: In this study, Lemaitre and colleagues have analyzed plasmatocyte-specific transcriptome profiles of Hml-positive plasmatocytes in the wild type, after clean injury, or septic injuries with Gram(-) or Gram(+) bacterium. This study is especially interesting in that immediate early transcriptional changes upon injury or infection, which do not accompany the hemocyte differentiation and are important for initiating immune reactions, are profiled for the first time. Though single-cell transcriptome analyses recently performed by other groups show hemocyte-specific RNA expressions at a single cell level, this study supplements additional insights – septic injuries in particular – into our current knowledge and itself will be a valuable resource to the community in the future.

1. The authors profiled Hml+ plasmatocytes by FACS sorting and compared transcriptomes of wild-type whole larvae to unchallenged hemocytes, and showed that there are hemocyte-specific gene expressions. However, some of the hemocyte-specific genes isolated in Hml+ hemocyte/whole larvae comparison could have been arisen due to the FACS sorting. To validate that FACS sorting itself does not influence the gene expression (or does exert minor alterations), although the hemocyte lineage is not changed, it will be important to add unsorted whole hemocyte transcriptome as an extra control. Several studies have released the whole RNA transcriptome of wild-type hemocytes, including the recent single-cell studies (Tattikota et al., and Cattenoz et al.,), and data are readily available for the analysis.

2. Though 45 minutes is short and may not be enough to induce differentiation of plasmatocytes, the proportion or the level of Hml may already be declining upon clean or septic injury. Is the level or the proportion of Hml+ plasmatocytes unchanged upon injury or infection? While manipulating the FACS sorting, the authors might have already noticed the information in the FACS sorting window as shown in Fig1.

1. “Drosophila” needs to be italicized in line 37, 46, 73, and others.

2. line 122: 20.000, 30.000 period to a comma

3. Please match the way of writing numbers; for example, line 146: 6723, line 153: 4,477

4. add references in line 289.

5. In addition to the comparisons between EcH/clean injury and SaH/clean injury, which identify EcH- or SaH-specific gene expressions, it will be interesting to compare EcH and SaH to provide ample information on the common immune responsive genes.

6. I assume that all the analyses were done with circulating hemocytes, not the total hemocytes including sessile populations. I might have missed, but there is no information as to which population in specific was used in this study.

Reviewer #3: The manuscript by Ramond et al describes the transcriptome of Drosophila hemocytes after clean wounding and immunization with E.coli and S.aureus compared to unchallenged samples. Thus the manuscript joins a series of papers/manuscripts with a similar focus (Cho et al; Tattikota et al; Cattenoz et al), which are cited by the authors. This includes the manuscripts on BioArchives. The similarities between the studies are indicated, so altogether this is a relevant and important contribution to cellular insect immunology. One drawback of this study – admitted by the authors - is the use of only one time point for collecting hemocytes. I only wondered whether when discussing FGF signaling and Pyramus, one should refer to the Tattikota manuscript, since the authors also observe FGF signaling (breathless and branchless) as an important contribution to hemocyte activation. One important point: I could not access supplemental Figs. S4 and following and Fig. S3 does not seem to be labeled. Maybe there was a problem with downloading, although I tried different browsers. So my judgment is based on the GO enrichments.

Minor comments:

I would include the reference from the Hultmark group (Anderl et al) both when it comes to transdifferentiation/hemocyte subpopulations and perhaps some of the methodological aspects, for example they optimized FACS.

Line 80, should the second Rizki be capital?

Line 99; Fu et al. lacks year and the reference look strange in the RefList

Line 162: here I wonder about secreted immune proteins that lack a signal peptide, it is mentioned later on (when discussing Attacin-D) but since leaderless proteins are a major fraction of insect immune proteins…?

Line 264: I think lectin function during fly development is better characterized maybe one such say “immune function of lectins in flies is poorly characterized”

In Fig 2B ‘Number of elements: specific (1) or shared by 2, 3, ... lists’. The numbers are not clearly visible perhaps changing the Font color to black might make it easier to see.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

10 Jun 2020

Lausanne, June 2nd 2020,

Dear Andreas,

I hope you are doing well.

Thank you so much for taking care of our manuscript entitled ‘Comparative RNA-Seq Analyses of Drosophila Plasmatocytes Reveal Gene Specific Signatures In Response To Clean Injury And Septic Injury (PONE-D-20-12312). We are happy to see that all the reviewers are positive about our work. We find their suggestions fair and helpful. We have addressed nearly all their comments in the revised version and hope that you will find these modifications to the manuscript satisfactory.

We would like to thank the reviewers for their careful reading of the manuscript and their constructive suggestions. We believe that our work, although descriptive by essence, will be useful for the community in the aim of better characterizing hemocyte function. You will find below our point-by-point responses to the reviewers’ comments. We hope that this will help you in considering the manuscript for publication in PLOS ONE.

With best regards,

Elodie Ramond and Bruno Lemaitre

Answer to reviewers

Answers are written in green, modified and new figures are highlighted in yellow and added text in the manuscript in red.

We adapted all the references according to PLoS ONE guidelines. This led to a change in lines number. We apologize for this, and indicate the change between the old-line numbers and the new line numbers.

Referee #1

The experimental set-up is simple, straight forward and strategic. All possible combinations were investigated and the findings well discussed in the light of the literature. The datasets will provide a basis for further studies on possible differential interaction among hemocyte (plasmatocyte) subsets in the immune response to S. aureus and E. coli. The studies comprehensive describe the functions of the differentially expressed genes and activation networks. The figures are of good quality and informative.

We thank the reviewer for the positive appreciation of our work.

The differences in gene expression pattern in the response to S. aureus and E. coli could be elaborated a bit more. Maybe a figure (direct comparison) could help to make the findings and conclusions more transparent.

R1 / We agree with the reviewer that this part of the manuscript could be better illustrated. In the revised version, we have added a new figure (Fig 3) that highlights the similarities and differences in gene expression in these two settings. This figure is mentioned in the text at lines 412 and 424, and its description in line 427. Of note this re-analysis led us to mention in further detail the Gbp1 gene in the result section. The added text (in line 461) is “We also observed an increase in expression of Growth blocking peptide 1 (Gbp1). Gbp1 was identified as a cytokine that plays a role in IMD pathway activation upon Gram-negative bacterial challenge in the fat body and hemocytes [118]. Our study rather points to a specific induction of Gbp1 in response to a Gram-positive bacterial challenge”

Referee #2

In this study, Lemaitre and colleagues have analyzed plasmatocyte-specific transcriptome profiles of Hml-positive plasmatocytes in the wild type, after clean injury, or septic injuries with Gram(-) or Gram(+) bacterium. This study is especially interesting in that immediate early transcriptional changes upon injury or infection, which do not accompany the hemocyte differentiation and are important for initiating immune reactions, are profiled for the first time. Though single-cell transcriptome analyses recently performed by other groups show hemocyte-specific RNA expressions at a single cell level, this study supplements additional insights – septic injuries in particular – into our current knowledge and itself will be a valuable resource to the community in the future.

We are pleased by the reviewer’s positive reception of our manuscript.

1. The authors profiled Hml+ plasmatocytes by FACS sorting and compared transcriptomes of wild-type whole larvae to unchallenged hemocytes, and showed that there are hemocyte-specific gene expressions. However, some of the hemocyte-specific genes isolated in Hml+ hemocyte/whole larvae comparison could have been arisen due to the FACS sorting. To validate that FACS sorting itself does not influence the gene expression (or does exert minor alterations), although the hemocyte lineage is not changed, it will be important to add unsorted whole hemocyte transcriptome as an extra control. Several studies have released the whole RNA transcriptome of wild-type hemocytes, including the recent single-cell studies (Tattikota et al., and Cattenoz et al.,), and data are readily available for the analysis.

R2 / We understand the concerns of the reviewer. The FACS machine was cooled down for the experiment. During the cell sorting, cells were immediately transferred in Eppendorf tubes kept on ice, and treated with Trizol within minutes. This was done to greatly minimize any further transcriptional changes after the dissection. We added in the Material and methods section, in line 614, the following sentence: « The machine temperature was lowered at 4°C, samples were recovered in Eppendorf tubes kept on ice and immediately resuspended in TRIzol™ (#15596018, Thermo Fisher Scientific, Waltham, MA, USA). » In addition, we underline in the manuscript similarities between our finding and the one reported by Irving et al., Cellular microbiology (2005) and Cattenoz et al., 2020. This makes us confident about the value of our data.

2. Though 45 minutes is short and may not be enough to induce differentiation of plasmatocytes, the proportion or the level of Hml may already be declining upon clean or septic injury. Is the level or the proportion of Hml+ plasmatocytes unchanged upon injury or infection? While manipulating the FACS sorting, the authors might have already noticed the information in the FACS sorting window as shown in Fig1.

R3 / We agree that 45 min can be considered as a short time in our model, however, we observed that larvae pricked with fluorescent bacteria carry hemocytes with internalized pathogens at 45 min. This led us to choose 45 min as a time point, to observe early transcriptional modifications.

The proportion of hemocytes is globally very similar in our different settings, either under unchallenged, clean-injured or pricked with bacteria. We added in the Figure 2 the corresponding counts of hemocytes for each experiment.

1. “Drosophila” needs to be italicized in line 37, 46, 73, and others.

2. line 122: 20.000, 30.000 period to a comma

3. Please match the way of writing numbers; for example, line 146: 6723, line 153: 4,477

R4 / We thank the reviewer for reading the text meticulously. We have now corrected the manuscript

4. add references in line 289.

R5 / We thank the reviewer for noticing this oversight. The line 289 corresponds now to the line 292. We have added in the manuscript a reference corresponding to the work from Geissman laboratory (ref #83).

5. In addition to the comparisons between EcH/clean injury and SaH/clean injury, which identify EcH- or SaH-specific gene expressions, it will be interesting to compare EcH and SaH to provide ample information on the common immune responsive genes.

R6 / We agree with the reviewer that a direct comparison of both gene signatures would help distinguish common and distinct responses and would clarify our purpose. For this, we added a figure (Fig 3) that defines the similarities and differences in gene expression in these two settings. This figure is mentioned in the text at lines 412 and 424, and its description in line 427.

6. I assume that all the analyses were done with circulating hemocytes, not the total hemocytes including sessile populations. I might have missed, but there is no information as to which population in specific was used in this study.

R7 / As we bled larvae without any disturbance prior dissection, the blood cell population corresponds to circulating hemocytes. We added in line 109: “The collected hemocytes thus correspond to circulating hemocytes.“

Referee #3

The manuscript by Ramond et al describes the transcriptome of Drosophila hemocytes after clean wounding and immunization with E.coli and S.aureus compared to unchallenged samples. Thus the manuscript joins a series of papers/manuscripts with a similar focus (Cho et al; Tattikota et al; Cattenoz et al), which are cited by the authors. This includes the manuscripts on BioArchives. The similarities between the studies are indicated, so altogether this is a relevant and important contribution to cellular insect immunology.

We thank the reviewer for commenting in a positive manner our work.

One drawback of this study – admitted by the authors - is the use of only one time point for collecting hemocytes. I only wondered whether when discussing FGF signaling and Pyramus, one should refer to the Tattikota manuscript, since the authors also observe FGF signaling (breathless and branchless) as an important contribution to hemocyte activation.

R8 / As suggested by the reviewer, we have now mentioned in the revised version the work of Tattikota et al. concerning the FGF pathway. In line 370, we added the following sentence:

“One this line, Tattikota et al. observed an enrichment of transcripts encoding the FGF ligand Branchless and receptor Breathless in crystal cells and lamellocytes subsets respectively, and revealed a role of the FGF pathway in the encapsulation of parasitoid wasps [37].”

This, together with our study, suggests an important role of FGF pathways in hemocyte differentiation.

One important point: I could not access supplemental Figs. S4 and following and Fig. S3 does not seem to be labeled. Maybe there was a problem with downloading, although I tried different browsers. So my judgment is based on the GO enrichments.

While this was written in the text, there were no Figs S3 and S4. We apologize for this mistake.

Minor comments:

I would include the reference from the Hultmark group (Anderl et al) both when it comes to transdifferentiation/hemocyte subpopulations and perhaps some of the methodological aspects, for example they optimized FACS.

R9 / As suggested by the reviewer, we added this reference in line 73, réf #34.

Line 80, should the second Rizki be capital?

R10 / We apologize for this annotation. We changed it to a classical writing in the core text and in the reference.

Line 99; Fu et al. lacks year and the reference look strange in the RefList

R11 / We corrected this mistake, now in line 759.

Line 162: here I wonder about secreted immune proteins that lack a signal peptide, it is mentioned later on (when discussing Attacin-D) but since leaderless proteins are a major fraction of insect immune proteins…?

R12 / The discussed section by the reviewer is now in line 151. We identified the set of ‘secreted proteins’ by selecting for the GO-terms ‘extracellular region’ and ‘extracellular space’. To our knowledge these GO-terms are attributed to proteins independently of the presence of a signal peptide. For example, Prophenoloxidases which play important immune functions lack a signal peptide and are secreted via an unknown process. Prophenoloxidases are still tagged with the GO-term extracellular region and are therefore included in our analysis (PPO2, PPO3). Additionally, the mentioned AMP Attacin-D also lacks a signal peptide but was still picked up in our analysis using the GO terms ‘extracellular region’ and ‘extracellular space’. Nevertheless, this broad Flybase classification does not guarantee that AttacinD is indeed secreted.

We added in line 1136 the following sentence: « which identifies putative secreted proteins regardless of the presence of a signal peptide ».

Line 264: I think lectin function during fly development is better characterized maybe one such say “immune function of lectins in flies is poorly characterized”

R13 / Indeed, we modified our claim as suggested by the reviewer, in line 268 by writing “The immune function of lectins is poorly characterized in Drosophila”

In Fig 2B ‘Number of elements: specific (1) or shared by 2, 3, ... lists’. The numbers are not clearly visible perhaps changing the Font color to black might make it easier to see.

R14 / To address this issue, we have changed the Font color to black and increased the size letter as suggested by the reviewer.

References mentioned in this text

37. Tattikota SG, Cho B, Liu Y, Hu Y, Barrera V, Steinbaugh MJ, et al. A single-cell survey of Drosophila blood. Elife. eLife Sciences Publications Limited; 2020;9: 597. doi:10.7554/eLife.54818

118. Tsuzuki S, Ochiai M, Matsumoto H, Kurata S, Ohnishi A, Hayakawa Y. Drosophila growth-blocking peptide-like factor mediates acute immune reactions during infectious and non-infectious stress. Sci Rep. Nature Publishing Group; 2012;2: 210–10. doi:10.1038/srep00210

Submitted filename: Response to Reviewers Ramond, Dudzic and Lemaitre.docx

Click here for additional data file.

12 Jun 2020

Comparative RNA-Seq Analyses of Drosophila Plasmatocytes Reveal Gene Specific Signatures In Response To Clean Injury And Septic Injury

PONE-D-20-12312R1

Dear Bruno,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Thank you for sending your work to PLOS ONE!

Kind regards,

Andreas

**********

Andreas Bergmann, Ph.D.

Academic Editor

PLOS ONE

16 Jun 2020

PONE-D-20-12312R1

Comparative RNA-Seq Analyses of Drosophila Plasmatocytes Reveal Gene Specific Signatures In Response To Clean Injury And Septic Injury

Dear Dr. Lemaitre:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Andreas Bergmann

Academic Editor

PLOS ONE

Table 1

Selected DEGs of interest with Fold-changes of unchallenged hemocytes vs. whole larvae from S2 File.

CG	Full name	FC	CG	Full name	FC
Hemocyte marker			Stress response
CG31770	Hemese	49.05	CG31359	Heat-shock protein-70Bb	55.07
CG3978	pannier	15.63	CG4183	Heat-shock protein-26	7.69
CG12002	Peroxidasin	15.51	CG11637	Ninjurin B	5.96
CG16707	visgun	13.62	CG4463	Heat-shock protein-23	4.43
CG3992	serpent	7.06	CG12101	Heat-shock protein-60A	4.36
CG7002	Hemolectin	6.76	CG4466	Heat-shock protein-27	4.27
CG2762	u-shaped	6.66	CG5436	Heat-shock protein-68	3.48
CG18426	yantar	2.73	CG4147	Heat shock 70-kDa protein cognate 3	3.3
Immune response			CG34246	Heat shock protein cognate 20	2.68
Antimicrobial defense			CG1242	Heat shock protein 83	2.59
CG4437	PGRP-LF	3.49	Oxidative stress response
CG4432	PGRP-LC	3.23	Glutathione S transferase family
CG5576	Immune deficiency	3.29	CG6776	Glutathione S transferase O3	10.43
CG8995	PGRP-LE	2.78	CG10045	Glutathione S transferase D1	8.55
CG11992	Relish	2.59	CG17523	Glutathione S transferase E2	8.51
CG32042	PGRP-LA	2.27	CG12242	Glutathione S transferase D5	5.22
CG6134	spatzle	10.5	CG4371	Glutathione S transferase D7	4.89
CG5974	pelle	5.31	CG4381	Glutathione S transferase D3	3.32
CG10520	tube	3.13	CG17530	Glutathione S transferase E6	3.12
CG7629	Attacin-D	7.8	CG11784	Glutathione S transferase E13	3.09
Melanization			CG5224	Glutathione S transferase E11	2.79
CG42640	Prophenoloxidase 3	25.59	CG10091	Glutathione S transferase D9	2.46
CG18550	yellow-f	13.55	CG5164	Glutathione S transferase E1	2.35
CG1102	Melanization protease 1	11.28	CG9362	Glutathione S transferase Z1	2.1
CG8193	Prophenoloxidase 2	9.1	CG30000	Glutathione S transferase T1	2
CG11331	Serpin 27A	7.93	Others genes
CG7219	Serpin 28Dc	3.27	CG32495	Glutathione synthetase 2	20.18
Antiviral immunity			CG17753	Copper chaperone for superoxide dismutase	3.62
CG7138	r2d2	2.22	CG8905	Superoxide dismutase 2 (Mn)	3.57
Phagocytic receptors and markers			CG11793	Superoxide dismutase 1	2.8
CG4099	Scavenger receptor	48.99	CG1274	thioredoxin peroxidase 2	2.47
CG8942	Nimrod C1	26.55	CG1633	thioredoxin peroxidase 1	2.03
CG6124	eater	15.35	Metabolism
CG2086	draper	3.19	Lipid metabolism / Peroxisome
CG10106	Tetraspanin 42Ee	3.48	CG7291	Niemann-Pick type C-2a	7.59
CG4591	Tetraspanin 86D	4	CG3083	Peroxin 19	6.67
CG6120	Tetraspanin 96F	2.9	CG3639	Peroxin 12	6.06
CG10742	Tetraspanin 3A	3.2	CG7081	Peroxin 2	4.44
Opsonins			CG7864	Peroxin 10	2.94
CG33119	Nimrod B1	23.27	CG3947	Peroxin 16	2.41
CG33115	Nimrod B4	17.69	CG4289	Peroxin 14	2.2
CG16873	Nimrod B5	3.78	CG3415	peroxisomal Multifunctional enzyme type 2	2.27
CG2958	lectin-24Db	33.93	CG6783	fatty acid binding protein	9.73
CG7106	lectin-28C	24.47	CG4280	croquemort	4.74
CG18096	Thioester-containing protein 1	19.2	Adenosine metabolism
CG5210	Imaginal disc growth factor 6	6.6	CG5992	Adenosine deaminase-related growth factor A	6.59
CG4472	Imaginal disc growth factor 1	5.91	CG11994	Adenosine deaminase	5.59
Cellular encapsulation			Glucose metabolism
CG14225	eye transformer	17.16	CG14816	Phosphoglycerate mutase 5	4.87
CG3715	SHC-adaptor protein	7.03	CG6453	Glucosidase 2 beta subunit	2.11
CG7830	Oligosaccharide transferase gamma subunit	2.68	CG7010	Pyruvate dehydrogenase E1 alpha subunit	2.06
			CG30410	Ribose-5-phosphate isomerase	2.04

Table 2

Selected DEGs of interest with Fold-changes of clean injury hemocytes vs. unchallenged hemocytes from S5 File.

CG	Full name	FC	CG	Full name	FC
Immune response			Gene regulation
Antimicrobial defense			CG13194	pyramus	31.23
CG11992	Relish	4.51	CG10045	Daughters against dpp	4.82
CG43720	sickie	4.12	CG33542	unpaired 3	4.26
CG14704	Peptidoglycan recognition protein LB	3.21	CF13780	PDGF- and VEGF-related factor 2	3.87
CG4437	Peptidoglycan recognition protein LF	2.44	CG4371	wallenda (MAP Kinase Kinase)	2.60
CG1399	Leucine-rich repeat	2.62	CG32406	PVR adaptor protein	2.53
CG16712	Immune induced molecule 33	2.54	CG15154	Suppressor of cytokine signaling at 36E	2.52
CG5848	cactus	2.18	CG7850	puckered	2.30
CG1878	Cecropin B	13.04	Cytoskeleton organization
CG1367	Cecropin A2	8.38	CG3259	Intraflagellar transport 54	14.41
CG1373	Cecropin C	6.85	CG11798	charlatan	7.49
CG18372	Attacin-B	6.86	CG4843	Tropomyosin 2	7.46
Clotting			CG5372	Integrin alphaPS5 subunit	7.19
CG15825	fondue	4.23	CG12008	karst	6.75
CG7002	Hemolectin	2.23	CG5695	jaguar	6.04
Phagocytosis			CG43976	Rho guanine nucleotide exchange factor 3	4.98
CG31962	Scavenger receptor	2.74	CG1212	p130CAS	4.85
Melanization			CG8865	Ral guanine nucleotide dissociation stimulator-like	4.15
CG10697	Dopa decarboxylase	15.09	CG10076	spire	4.08
CG9441	Punch	3.32	CG33103	Papillin	4.06
Repair			CG13503	Verprolin 1	3.60
CG2043	Larval cuticle protein 3	19.21	CG3937	cheerio	3.28
CG4859	Matrix metalloproteinase 1	9.77	CG11949	coracle	3.24
CG2044	Larval cuticle protein 4	3.84	CG14396	Ret oncogene	3.18
CG11086	Growth arrest and DNA damage-inducible 45	2.57	CG33558	missing-in-metastasis	3.06
CG33956	kayak	2.18	CG2184	Myosin light chain 2	3.02
Others			CG10119	Lamin C	2.73
CG8588	pastrel	3.10	CG18214	trio	2.68
CG6821	Larval serum protein 1 gamma	8.35	CG33694	CENP-ana	2.58
Stress response			CG5164	cappuccino	2.41
Heat shock proteins			CG9362	kugelei	2.40
CG5834	Heat-shock protein-70Bbb	16.44	CG10522	sticky	2.38
CG6489	Heat-shock protein-70Bc	15.67	CG18076	short stop	2.34
CG18743	Heat-shock-protein-70Ab	10.25	CG1560	myospheroid	2.12
CG31359	Heat-shock protein-70Bb	8.78	CG42274	Rho GTPase activating protein at 18B	2.11
CG5436	Heat-shock protein-68	6.28	CG1520	WASp	2.1
Cyp450			Vesicle trafficking
CG10245	Cyp6a20	10.59	CG8024	Rab32	3.15
CG10241	Cytochrome P450-6a17	3.18	CG14001	blue cheese	2.34
CG10242	Cyp6a23	2.83	CG14296	Endophilin A	2.15
Others			Cell death effectors
CG9434	Frost	9.19	CG1600	Death resistor Adh domain containing target	3.63
CG32130	starvin	8.80	CG33134	Death executioner Bcl-2	2.33
CG12703	Peroxisomal Membrane Protein 70 kDa	4.61	Metabolism
CG14709	Multidrug resistance protein 4	2.85	Glucose metabolism
CG10578	DnaJ-like-1	2.54	CG5932	magro	26.43
CG6449	Ninjurin A	2.46	CG34360	Glucose transporter 4 enhancer factor	8.97
CG31216	Nicotinamide amidase	2.44	CG5889	Malic enzyme b	4.28
Oxidative stress response			CG4625	Dihydroxyacetone phosphate acyltransferase	4.24
Glutathione S transferase family			CG6906	Carbonic anhydrase	3.70
CG4181	Glutathione S transferase D2	10.36	CG8256	Glycerophosphate oxidase 1	2.69
CG5164	Glutathione S transferase E1	5.60	Lipid catabolism
CG4381	Glutathione S transferase D3	4.19	CG3620	no receptor potential A	2.25
CG17524	Glutathione S transferase E3	4.19	CG11055	Hormone-sensitive lipase	2.22
CG17533	Glutathione S transferase E8	3.21	CG1882	pummelig	2.05
CG4371	Glutathione S transferase D7	3.20	Amino acid storage
Others			CG6821	Larval serum protein 1 gamma	8.35
CG2259	Glutamate-cysteine ligase catalytic subunit	3.56	CG4178	Larval serum protein 1 beta	3.54
			CG2559	Larval serum protein 1 alpha	2.69

Table 3

Selected DEGs of interest with Fold-changes of E. coli hemocytes vs. Clean injury hemocytes from S6 File.

CG	Full name	FC
Immune related genes—cellular immunity
CG8175	Metchnikowin	7.31
CG10794	Diptericin B	4.97
CG9496	Tetraspanin 29Fb	4.53
CG1364	Cecropin A1	4.04
CG1367	Cecropin A2	3.33
CG32185	Elevated during infection	3.17
CG10146	Attacin-A	2.74
CG7629	Attacin-D	2.72
CG16844	Bomanin Short 3	2.67
CG1373	Cecropin C	2.52
CG14704	Peptidoglycan recognition protein LB	2.07
Cell migration / cytoskeleton reorganization
CG31004	mesh	3.47
CG6976	Myosin 28B1	2.03
Stress response
CG9434	Frost	2.99
CG3050	Cyp6d5	2.42
Glucose metabolism
CG8693	Maltase A4	4.59
CG11909	Target of brain insulin	3.89

Table 4

Selected DEGs of interest with Fold-changes of S. aureus hemocytes vs. Clean injury hemocytes from S7 File.

CG	Full name	FC
Immune related genes—cellular immunity
CG32185	elevated during infection	3.96
CG8175	Metchnikowin	3.57
CG12143	Tetraspanin42Ej	2.37
CG15917	Growth-blocking peptide 1	2.29
CG10794	Diptericin B	2.19
CG16844	Bomanin Short 3	2.08
CG12840	Tetraspanin 42El	2.02
Cell migration / cytoskeleton reorganization
CG32082	Insulin receptor substrate 53 kDa	2.71
Extracellular matrix components
CG6281	Tissue inhibitor of metalloproteases	2.67
CG42768	Muscle-specific protein 300 kDa	2.55
Stress response–cell death
CG4319	reaper	2.59
CG10391	Cyp310a1	2.43
CG8453	Cyp6g1	2.40
Oxidative stress response
CG12242	Glutathione S transferase D5	2.02