Transcriptome Analyses of Diaphorina citri Midgut Responses to Candidatus Liberibacter Asiaticus Infection

The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Liviidae), is an important transmission vector of the citrus greening disease Candidatus Liberibacter asiaticus (CLas). The D. citri midgut exhibits an important tissue barrier against CLas infection. However, the molecular mechanism of the midgut response to CLas infection has not been comprehensively elucidated. In this study, we identified 778 differentially expressed genes (DEGs) in the midgut upon CLas infection, by comparative transcriptome analyses, including 499 upregulated DEGs and 279 downregulated DEGs. Functional annotation analysis showed that these DEGs were associated with ubiquitination, the immune response, the ribosome, endocytosis, the cytoskeleton and insecticide resistance. KEGG enrichment analysis revealed that most of the DEGs were primarily involved in endocytosis and the ribosome. A total of fourteen DEG functions were further validated by reverse transcription quantitative PCR (RT-qPCR). This study will contribute to our understanding of the molecular interaction between CLas and D. citri.


Introduction
Huanglongbing (HLB) is a destructive disease of citrus that represents a major threat to the world's citrus industry. HLB is caused by the phloem-limited, Gram-negative bacterium "Candidatus Liberibacter spp" (CLas). HLB nearly destroyed the citrus industry in Florida (USA) and most citrus-producing regions of the world [1,2]. CLas can decrease plant growth vigor, and ultimately result in the death of the infected citrus tree [3]. There are no effective methods to control HLB once established. Recently, many antibiotics have been adopted to control CLas bacteria; examples include the trunk injection of penicillin, streptomycin and oxytetracycline hydrochloride [1,4]. However, these approaches do not work well in the field. The killing of vectors is an effective method to reduce HLB spread [5].
The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, is the principal transmission vector of HLB [6]. Vector control of D. citri is recognized as a key approach to preventing the spread of HLB. The application of insecticides is the most widely employed option for reducing D. citri populations in some citrus-growing regions [7]. However, the improper use of such chemicals has caused the specific primer set OI1/OI2c (forward primer 5 -GCGCGTATGCAATACGAGCGGCA-3 and reverse primer 5 -GCCTCGCGACTTCGCAACCCAT-3 ) based on a previous protocol [24]. Then, agarose gel electrophoresis was performed to investigate the CLas infection rate. When the CLas-infected percentage reached above 80%, the infected D. citri were selected. The uninfected and CLas-infected D. citri were collected and dissected to obtain the midgut, which was then washed with precooled DEPC-water to remove the remaining tissues. Genomic DNA was isolated from the D. citri midgut, and a PCR was performed to confirm the CLas infection rate.
For transcriptome sequencing, three groups, each consisting of three hundred CLas-infected or uninfected D. citri, were dissected for RNA extraction. In order to avoid RNA degradation, the collected midgut samples were stored in RNAlater and frozen at −80 • C. Total RNA was extracted from the midgut of uninfected and CLas-infected D. citri using an animal tissue total RNA kit (Simgen, Hangzhou, China). RNA concentration and purity were assayed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, New York, NY, USA) at absorbance ratios of A 260/280 and A 260/230 . The integrity of total RNA was confirmed using agarose gel electrophoresis.

Library Preparation and Illumina Sequencing
Transcriptome sequencing was conducted on the Illumina HiSeq platform (Novogene Bioinformatics Technology Co., Ltd. Tianjin, China). Sequencing libraries were generated using the NEBNext1Ultra™ RNA Library Prep Kit for Illumina1 (NEB, Ipswich, MA, USA). The library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kitv3-cBot-HS (Illumina, San Diego, CA, USA). After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform, and 125 bp paired-end reads were generated. The sequencing fragments were translated into fastq format raw reads using the CASAVA software. The raw reads of fastq format were firstly processed through in-house Perl scripts. The clean reads were obtained by removing reads containing adapter, reads containing poly-N, and low quality reads in which a base number of Qphred less than or equal to twenty accounted for more than fifty percent of the entire read length. At the same time, Q20, Q30 and the GC content of the clean data were calculated.

Read Mapping and Identification of DEGs
The reference genome and gene model annotation files were downloaded from the genome website (ftp://ftp.citrusgreening.org/annotation/OGSv2.0/) [25]. Hisat2 v2.0.5 (https://anaconda.org/ biobuilds/hisat2) was used to build the index of the reference genome and align the paired-end clean reads with the reference genome [26]. This generated a database of splice junctions based on the gene model annotation file. The expression levels of the genes were calculated by fragments per kilobase of transcript per million fragments mapped (FPKM). Differential expression analysis between CLas-free groups and CLas-infected groups were performed using the DESeq2 R package (1.16.1). DESeq2 provides statistical routines for determining differential expression according to the negative binomial distribution. The resulting P-values were adjusted using the Benjamini-Hochberg method. A Corrected Genes P-value of 0.05 and an absolute |log2 (fold change)| of 0 were set as the threshold for significantly differential expression. The hierarchical cluster analysis of differentially expressed genes (DEGs) was conducted using Genesis software (http://genome.tugraz.at/genesisclient_download.shtml).

Gene Ontology (GO) and KEGG Enrichment Analysis of DEGs
Gene Ontology (GO) is a tool used for gene annotation by collecting defined, structured, controlled vocabulary [27]. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database used to categorize associated gene sets into appropriate pathways [28]. The clusterProfiler R package, which implements the GO terms, was used for the enrichment analysis of DEGs, in which the gene lengths of these DEGs were corrected [29]. KEGG pathway enrichment analysis for DEGs was performed using the clusterProfiler R package [30]. A P-value of < 0.01 was set as the threshold.

Reverse Transcription Quantitative PCR (RT-qPCR) Analysis
In order to verify the reliability of the transcriptome data, the expression levels of 14 randomly selected DEGs were examined by RT-qPCR. All of the primers used are listed in Table S1. PCR reactions were prepared containing 10 µL of SYBR II, 8 µL of ddH 2 O, 0.5 µL of forward primer, 0.5 µL of reverse primer and 1.0 µL of cDNA template. The reactions were performed on the LightCycle ® 96 PCR Detection System (Roche, Basel, Switzerland). The relative expression level of each gene was calculated using the 2 −∆∆Ct method. Three biological replicates were conducted for each sample. D. citri glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene. The expression patterns were compared by ANOVAs, followed by Fisher's protected least significant difference (LSD) tests [31].

Detection of CLas
Infection in the Midgut of D. citri D. citri midguts were dissected in DEPC-water using a dissecting needle. The results showed that the detached midguts were relatively intact ( Figure 1A). To ensure the integrity of the midgut samples for transcriptome sequencing, genomic DNA was isolated, and PCR was performed using the OI1 primer. The primer sequences are shown in Table 1. Analysis of the PCR products on agarose gels showed that the CLas-infected midguts exhibited a clear OI1 band, of 1160 bp in length, that was not present in the control group ( Figure 1B) [24]. These results indicated that the selected midgut samples could be used for further transcriptome sequencing.

Reverse Transcription Quantitative PCR (RT-qPCR) Analysis
In order to verify the reliability of the transcriptome data, the expression levels of 14 randomly selected DEGs were examined by RT-qPCR. All of the primers used are listed in Table S1. PCR reactions were prepared containing 10 μL of SYBR Ⅱ, 8 μL of ddH2O, 0.5 μL of forward primer, 0.5 μL of reverse primer and 1.0 μL of cDNA template. The reactions were performed on the LightCycle®96 PCR Detection System (Roche, Basel, Switzerland). The relative expression level of each gene was calculated using the 2 −∆∆Ct method. Three biological replicates were conducted for each sample. D. citri glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene. The expression patterns were compared by ANOVAs, followed by Fisher's protected least significant difference (LSD) tests [31].

Detection of CLas Infection in the Midgut of D. citri
D. citri midguts were dissected in DEPC-water using a dissecting needle. The results showed that the detached midguts were relatively intact ( Figure 1A). To ensure the integrity of the midgut samples for transcriptome sequencing, genomic DNA was isolated, and PCR was performed using the OI1 primer. The primer sequences are shown in Table 1. Analysis of the PCR products on agarose gels showed that the CLas-infected midguts exhibited a clear OI1 band, of 1160 bp in length, that was not present in the control group ( Figure 1B) [24]. These results indicated that the selected midgut samples could be used for further transcriptome sequencing.     (Table 1). Therefore, the accuracy of the sequencing data was sufficient for further analysis.

Identification of DEGs in Response to CLas Infection
In total, 798 DEGs were identified in the midgut of CLas-infected D. citri compared with the uninfected D. citri. Among these DEGs, 499 were upregulated and 279 were downregulated ( Figure 2A). Basing on the log10 (RPKM+1) values of the two groups, we performed hierarchical clustering of all the DEGs to determine the expression patterns of the identified genes ( Figure 2B). These results indicated that CLas infection altered the transcriptional profiles of the DEGs.

Identification of DEGs in Response to CLas Infection
In total, 798 DEGs were identified in the midgut of CLas-infected D. citri compared with the uninfected D. citri. Among these DEGs, 499 were upregulated and 279 were downregulated ( Figure  2A). Basing on the log10 (RPKM+1) values of the two groups, we performed hierarchical clustering of all the DEGs to determine the expression patterns of the identified genes ( Figure 2B). These results indicated that CLas infection altered the transcriptional profiles of the DEGs.

GO and KEGG Enrichment Analysis
GO and KEGG enrichment analyses were conducted to further investigate the functions of the DEGs [32]. In this study, a total of 106 upregulated DEGs and 73 downregulated DEGs were performed for GO enrichment analysis. The GO enrichment analysis revealed that a total of 18 upregulated DEGs were mainly associated with the cytoplasm, 20 upregulated DEGs were associated with the organonitrogen compound metabolic process and 14 upregulated DEGs were related to the carbohydrate metabolic process ( Figure 3A, Table S2); a total of 20 downregulated DEGs were mainly involved in transmembrane transport and 14 downregulated DEGs were associated with transmembrane transporter activity ( Figure 3B, Table S2). KEGG pathway analysis is useful for researching the complex biological functions of genes [33]. According to the KEGG pathway enrichment analysis, a total of seven, three and four upregulated DEGs were significantly enriched in ribosome, proteasome and oxidative phosphorylation, respectively ( Figure 4A, Table S3). A total of six downregulated DEGs were significantly enriched in endocytosis ( Figure 4B, Table S3).
Insects 2020, 11, 171 6 of 16 GO and KEGG enrichment analyses were conducted to further investigate the functions of the DEGs [32]. In this study, a total of 106 upregulated DEGs and 73 downregulated DEGs were performed for GO enrichment analysis. The GO enrichment analysis revealed that a total of 18 upregulated DEGs were mainly associated with the cytoplasm, 20 upregulated DEGs were associated with the organonitrogen compound metabolic process and 14 upregulated DEGs were related to the carbohydrate metabolic process ( Figure 3A, Table S2); a total of 20 downregulated DEGs were mainly involved in transmembrane transport and 14 downregulated DEGs were associated with transmembrane transporter activity ( Figure 3B, Table S2). KEGG pathway analysis is useful for researching the complex biological functions of genes [33]. According to the KEGG pathway enrichment analysis, a total of seven, three and four upregulated DEGs were significantly enriched in ribosome, proteasome and oxidative phosphorylation, respectively ( Figure 4A, Table S3). A total of six downregulated DEGs were significantly enriched in endocytosis ( Figure 4B, Table S3).   GO and KEGG enrichment analyses were conducted to further investigate the functions of the DEGs [32]. In this study, a total of 106 upregulated DEGs and 73 downregulated DEGs were performed for GO enrichment analysis. The GO enrichment analysis revealed that a total of 18 upregulated DEGs were mainly associated with the cytoplasm, 20 upregulated DEGs were associated with the organonitrogen compound metabolic process and 14 upregulated DEGs were related to the carbohydrate metabolic process ( Figure 3A, Table S2); a total of 20 downregulated DEGs were mainly involved in transmembrane transport and 14 downregulated DEGs were associated with transmembrane transporter activity ( Figure 3B, Table S2). KEGG pathway analysis is useful for researching the complex biological functions of genes [33]. According to the KEGG pathway enrichment analysis, a total of seven, three and four upregulated DEGs were significantly enriched in ribosome, proteasome and oxidative phosphorylation, respectively ( Figure 4A, Table S3). A total of six downregulated DEGs were significantly enriched in endocytosis ( Figure 4B, Table S3).

Validation of DEGs at the Transcriptional Level
To validate the reliability of the transcriptome sequencing data, the relative expression levels of 14 DEGs involved in different functions were analyzed by RT-qPCR ( Figure 5). These results were consistent with the transcriptome data. For example, the gene E3 ubiquitin-protein ligase (DcitrP055490.1) was upregulated in both the transcriptome data and the RT-qPCR analysis, with a similar fold change. However, 40S ribosomal protein S9 (DcitrP013835.1) and heat shock protein 70 (DcitrP017415.1) were upregulated in the midgut after CLas infection, while they had no obvious change in the RT-qPCR analysis ( Figure 5A). The linear regression analysis of the correlation between RT-qPCR and transcriptome showed an R2 value of 0.936 and a corresponding slope of 1.0208 ( Figure 5B). These results suggested a significant correlation between RT-qPCR and the transcriptome data. To validate the reliability of the transcriptome sequencing data, the relative expression levels of 14 DEGs involved in different functions were analyzed by RT-qPCR ( Figure 5). These results were consistent with the transcriptome data. For example, the gene E3 ubiquitin-protein ligase (DcitrP055490.1) was upregulated in both the transcriptome data and the RT-qPCR analysis, with a similar fold change. However, 40S ribosomal protein S9 (DcitrP013835.1) and heat shock protein 70 (DcitrP017415.1) were upregulated in the midgut after CLas infection, while they had no obvious change in the RT-qPCR analysis ( Figure 5A). The linear regression analysis of the correlation between RT-qPCR and transcriptome showed an R2 value of 0.936 and a corresponding slope of 1.0208 ( Figure  5B). These results suggested a significant correlation between RT-qPCR and the transcriptome data. Figure 5. The correlation between the gene expression ratios obtained from the transcriptome data and RT-qPCR data. (A) The differential expression levels of 14 differentially expressed genes in the CLas-free and CLas-infected D. citri midgut. The relative expression levels were calculated using the 2 -∆∆Ct method. Statistical analysis was performed using the SPSS software. The significant differences are indicated by * (p < 0.05) or ** (p < 0.01); (B) Lineage analysis between the transcriptome and RT-qPCR data. The ratios obtained by RT-qPCR (Y-axis) were plotted against the ratios obtained by the transcriptome (X-axis).

Analysis of DEGs Associated with Ubiquitination, the Immune Response and the Ribosome
Based on the transcriptome analysis, many DEGs associated with ubiquitination, the immune response and ribosomes were altered in the uninfected groups and the CLas-infected groups (Table 2, Figure 6). For the ubiquitination analysis, a total of 13 DEGs were identified. Among them, eight DEGs (61.5%) were upregulated in the midgut after CLas infection. In addition, five DEGs were downregulated, including ubiquitin-associated domain-containing protein 1, ubiquitin-like modifier-activating enzyme ATG7, E3 ubiquitin-protein ligase RNF8, E3 ubiquitin-protein ligase MARCH2 and ubiquitin-conjugating enzyme E2J2. For the immune response analysis, 11 DEGs were obtained after CLas infection in the midgut; nine genes (81.8%) were upregulated except for Toll-like Figure 5. The correlation between the gene expression ratios obtained from the transcriptome data and RT-qPCR data. (A) The differential expression levels of 14 differentially expressed genes in the CLas-free and CLas-infected D. citri midgut. The relative expression levels were calculated using the 2 −∆∆Ct method. Statistical analysis was performed using the SPSS software. The significant differences are indicated by * (p < 0.05) or ** (p < 0.01); (B) Lineage analysis between the transcriptome and RT-qPCR data. The ratios obtained by RT-qPCR (Y-axis) were plotted against the ratios obtained by the transcriptome (X-axis).

Analysis of DEGs Associated with Ubiquitination, the Immune Response and the Ribosome
Based on the transcriptome analysis, many DEGs associated with ubiquitination, the immune response and ribosomes were altered in the uninfected groups and the CLas-infected groups (Table 2, Figure 6). For the ubiquitination analysis, a total of 13 DEGs were identified. Among them, eight DEGs (61.5%) were upregulated in the midgut after CLas infection. In addition, five DEGs were downregulated, including ubiquitin-associated domain-containing protein 1, ubiquitin-like modifier-activating enzyme ATG7, E3 ubiquitin-protein ligase RNF8, E3 ubiquitin-protein ligase MARCH2 and ubiquitin-conjugating enzyme E2J2. For the immune response analysis, 11 DEGs were obtained after CLas infection in the midgut; nine genes (81.8%) were upregulated except for Toll-like 8B and Beat protein. For the ribosome analysis, a total of 15 DEGs were identified, and most of the genes were upregulated, except for 28S ribosomal protein S27.

Analysis of DEGs Involved in Endocytosis, the Cytoskeleton and Insecticide Resistance
Based on the GO and KEGG enrichment analyses, the DEGs involved in endocytosis, the cytoskeleton and insecticide resistance were identified (Table 3, Figure 7). For endocytosis, six (85.7%) DEGs were downregulated, and only one gene was upregulated. For the cytoskeleton analysis, a total of 15 genes were screened, and most of the proteins were upregulated after CLas infection. However, tyrosine protein kinase receptor torso and dynein heavy chain were downregulated. For insecticide resistance, a total of 19 DEGs were found; of these DEGs, 13 (68.4%) were upregulated and six (31.6%) were upregulated, in the midgut after CLas infection.  Figure 7. Hierarchical analysis for DEGs associated with endocytosis, the cytoskeleton and insecticide resistance between CLas-free and CLas-infected D. citri midgut. A hierarchical cluster analysis was performed using the Genesis software. DEG expression is shown with a pseudocolour scale (from −3 to 3), with the red colour indicating high expression levels and the green color indicating low expression. Each group represents three biological replicates. Table 3. Differentially expressed genes upon CLas infection involved in endocytosis, the cytoskeleton and insecticide resistance, comparing CLas-infected groups and CLas-free groups.

Discussion
HLB is devastating citrus production worldwide and belongs to a phloem-limited α-proteobacterium. However, the study of this bacterium is highly difficult due to the difficulties in culturing it in vitro [34]. The management of HLB depends on the removal of infected trees and the control of the D. citri. Chemical insecticides are currently employed as the primary management strategy, but the widespread use of insecticides leads to serious resistance in D. citri [35]. The study of the interaction between CLas and D. citri is promising for the control of HLB. The transmission of CLas in D. citri is a long-term process that is composed of an acquisition access period (APP), a period of latency and an inoculation access period (IAP) [13]. During CLas entry into the D. citri, the midgut is an important immune barrier for defense against bacterial infection. In a previous report, draft genome sequencing revealed that D. citri contained the Toll and JAK/STAT pathways, but lacked genes for the IMD pathway response to Gram-negative bacterial infection [36]. D. citri transmits CLas, beginning from a few days to a week after acquisition, and for the lifetime of the vector, suggesting that CLas have developed mechanisms to avoid psyllid cellular and humoral immune defenses. In Galleria mellonella, Bacillus thuringiensis infection significantly influenced antioxidant activity and the level of lipid peroxidation in the larval midgut [37]. Therefore, we considered that the D. citri midgut would play critical roles in the interaction between CLas and D. citri. In this study, we performed RNA-pooling sequencing, and a total of 62,582,728, 58,399,847 and 56,575,636 raw reads were obtained from CLas-free groups, and 57,180,938, 63,335,414 and 54,887,010 raw reads were obtained from CLas-infected groups. These results showed that the number of raw reads for CLas-free groups and CLas-infected groups were similar. Previous studies revealed that pooling sequencing did not represent the population variations in gene expression levels, but it could save costs and limit starting material. However, stringent false discovery rates (FDRs) and the high-throughput validation of DEGs should be considered [38]. After assembly, a total of 778 DEGs were identified in the midgut after CLas infection according to comparative transcriptome analysis. Many pathogens can induce the upregulation of host gene expression. Xiong et al. identified many immunity-related genes encoding pattern recognition receptors, signal modulators and immune effectors after the injection of the fungal pathogen Beauveria bassiana and the Gram-negative bacterium Enterobacter cloacae [39].
Tang et al. performed transcriptome analysis in Musca domestica larvae inoculated with a mixture of Escherichia coli and Staphylococcus aureus. The results showed that many genes involved in innate immunity were induced following infection [40]. In this study, KEGG enrichment analysis showed that upregulated genes were significantly enriched in ribosomes, oxidative phosphorylation and proteasomes. Downregulated genes were significantly enriched in endocytosis.

Ubiquitination, the Immune Response and Ribosomes May Play Important Roles in the Midgut Response to CLas Infection
Ubiquitination occurs through a series of reactions catalyzed by different enzymes, including Ub-activating E1, Ub-conjugating E2 and Ub-ligase E3 [41]. Ubiquitination plays an important role in the recognition and clearance of some invading bacteria. Many bacterial effectors enable them to interfere with the host's ubiquitination system, and thus to achieve successful infection [42]. Wang et al. confirmed that ubiquitin modification had multiple effects on the host immune system against Salmonella infection [43]. In this study, a total of 13 ubiquitination-related genes were identified; among them, eight genes were upregulated and five genes were downregulated after CLas infection. E3 ubiquitin-protein ligase was upregulated in the D. citri midgut following CLas infection. Zhang et al. revealed that zebrafish bloodthirsty member 20 with E3 ubiquitin ligase activity was involved in the immune response against bacterial infection [44]. Therefore, we speculated that E3 ubiquitin-protein ligase might play an important role in CLas infection. Many ubiquitinated proteins are recognized by the proteasome and are then further degraded [45]. The ubiquitin proteasome system is a key signaling pathway in the host response to bacterial or viral infection. Isaacson et al. revealed that host cells could utilize the ubiquitin-proteasome system to counteract viral infections through the generation of target structures recognized by T cells [46]. Yu et al. also identified eight differentially expressed proteins associated with ubiquitination in the B. mori midgut after B. mori nucleopolyhedrovirus (BmNPV) infection [47]. In this study, four proteasome-related proteins were upregulated after CLas infection, including proteasome activator complex subunit 3-like, proteasome subunit beta type, proteasome subunit alpha type and 26S proteasome non-ATPase regulatory subunit 12. We speculated that CLas bacteria invading the midgut might be recognized by the D. citri immune system. Meanwhile, CLas could activate host ubiquitination to eliminate immune-related proteins.
CLas, as Gram-negative bacteria, activate the host immune system after invading the midgut [48]. Thus, the midgut immune response may play an important role in the defense against pathogen infection [49]. In total, 11 genes related to immune responses were differentially expressed in the CLas-infected groups. Serine proteases (SPs) are involved in both the prophenoloxidase (PPO) activation cascade and the Toll immune signaling pathway, especially those with a clip domain. The proteases are secreted into the hemolymph as inactive precursors and transform into the active protein involved in the melanization reaction requiring specific proteolytic cleavage [50]. In this study, we found that three genes belonging to the serine protease family were upregulated after CLas infection, including CLIP domain-containing serine protease 2-like, CLIBP-serine protease 5 and CLIBP-serine protease 4, partial. In insects, PPO is activated through a serine protease cascade upon the recognition of pathogen-associated molecular patterns. In previous research, two PPOs were identified from the genome database of D. citri. We considered that CLIP domain-containing serine protease 2-like might regulate D. citri PPOs to activate melanization to defend against CLas infection [27].
In the process of host invasion by pathogens, many proteins are utilized to achieve replication. Ribosomal proteins, in conjunction with rRNA, make up the ribosomal subunits involved in the cellular process of translation [51]. In this study, a total of 15 DEGs related to ribosomes were screened, comparing the CLas-infected group to the CLas-free group. Among these genes, 14 were upregulated after CLas infection, except for 28S ribosomal protein S27 (mitochondrial). Yu et al. also found that some ribosome-associated proteins were upregulated after BmNPV infection [47]. These results indicate that ribosomal proteins are likely to play crucial roles in the response to CLas infection.

Endocytosis, the Cytoskeleton and Insecticide Resistance May Play Crucial Roles in the Midgut Response to CLas Infection
Clathrin-mediated endocytosis is mainly involved in the selective and facilitated internalization of cell surface receptors [52]. Many pathogens can take advantage of the endocytosis machinery in the cytosol for replication [53][54][55]. However, the specific mechanism of endocytosis between viruses and bacteria is different. Bacteria can secrete some proteins or components that allow the modification of the internalization vacuole to permit an intravacuolar lifestyle with concomitant replication [56]. For some viruses, endocytes can help transport incoming particles deep into the cytoplasm unobstructed by cytoplasmic crowding and obstacles such as the cytoskeleton [57]. Interestingly, seven genes associated with endocytosis were found, and most of them were downregulated after CLas infection, except for heat shock protein 70. During CLso infection of the potato psyllid, CLso cells were observed between the basal lamina and the basal epithelial cell membranes [58]. For example, ADP ribosylation factor proteins comprise a group of five Ras-related GTPases that are thought to function as regulators of membrane traffic. Interestingly, CLas are Gram-negative bacteria, but they are not pathogenic to D. citri. In addition, many studies revealed that CLas could be detected in the hemolymph from CLas-infected D. citri [23]. Therefore, we speculated that after CLas invading, D. citri might inhibit the expression of endocytosis-related genes in the midgut to prevent the further transmission of CLas. Moreover, CLas might avoid the host immune system by endocytosis.
The cytoskeleton is critical for the maintenance of cell shape, cell motility and intracellular transport, and bacterial and viral infections require the cytoskeleton [59]. Bacteria in the process of invasion and proliferation, at all stages of the intracellular bacterial life cycle, have the same three-dimensional cytosolic space containing the cytoskeleton [60]. In this instance, a total of 15 DEGs associated with the cytoskeleton were identified, and most were upregulated in the CLas-infected groups compared to the CLas-free groups. As a major protein constituent of cytoskeletal filaments, tubulin is involved in many vital cellular processes, including cell motility, cellular division and cytokinesis [61]. These results indicated that cytoskeleton-related genes played an important role in the process of CLas infection in D. citri.

Conclusions
By using transcriptome sequencing, we identified 778 genes differentially expressed in the midgut between the CLas-infected groups and CLas-free groups. KEGG and GO enrichment analyses revealed that 80 DEGs were associated with ubiquitination, the immune response, ribosomes, endocytosis, the cytoskeleton and insecticide resistance. This study provides a foundation for further research to investigate the mechanisms of CLas invasion of the D. citri midgut.

Conflicts of Interest:
The authors declare no conflict of interest.