Disease symptoms
An investigation in Gongliu County and Xinyuan County in the Yili region from 2017 to 2020 showed that bacterial perforation of wild apricot was serious, and the rate of diseased plants in the forest was 100%.
The symptoms of leaf spot-hole disease included damage to leaves, small spots in the shape of water stains in the initial stage, dark purple spots in the shape of water stains in the later stage, brown spots, light yellow-green halos at the junction of diseased and healthy tissues, and finally leaf perforation and loss. At the initial stage of damage, the surface of the fruit exhibited water stain-like, round brown spots, which began to yellow at the initial stage and expanded into brown spots at the later stage, with the edge of the spots curling up. There were obvious canker spots on the branches, and the bark at the disease spots was cracked (Fig. 1).
Morphological characteristics of isolates
A total of 97 leaf samples and 13 fruit samples were collected from Xinyuan County and Gongliu County in Yili. A total of 128 bacterial strains were obtained after isolation and purification. These strains can be divided into three types in terms of colony morphology. Class I colonies are white, round and opaque; Class II colonies are white, round, smooth and transparent; and Class III colonies are yellow, round, smooth and transparent. After the pathogenicity test, only the yellow strain was determined to be pathogenic. Representative strains GL9-2 and GL9-3 were selected for morphological observation. The colonies cultured on the NA plate for 24 h were yellow and round, with neat edges that were smooth and bright (Fig. 2-b). The colonies turned red after Gram staining and were determined to be a gram-negative bacterium (Fig. 2-a).
Pathogenicity of bacteria
The two representative strains were isolated, purified and preserved and then inoculated into leaves to observe their pathogenicity. The results of in vitro inoculation of wild apricot leaves showed that the two representative bacterial strains could cause disease in wild apricot leaves. One day after inoculation, water stains appeared on the leaf surface of wild apricot, and light-yellow spots the size of pinholes appeared at the inoculation point, which expanded in a circle. Seven days after inoculation, the lesion gradually expanded, and necrosis occurred in the center of the diseased site; 14 d after inoculation, the lesion continued to expand, the central necrotic part of the lesion fell off, and perforation began to occur (Fig. 3). The disease spots of inoculated, diseased leaves were separated again, and colonies with similar morphology to the tested strains were obtained, and there were no symptoms in the sterile water control. It was further proven that the tested strain could cause necrotic spots and perforation of wild apricot leaves. The representative strains could cause tobacco HR(Fig. 4).
Physiological and biochemical characteristics of pathogenic bacteria
Physiological and biochemical test results of strains GL9-2 and GL9-3 are shown in Table 3. Strains GL9-2 and GL9-3 were determined to be gram-negative bacteria that can liquefy gelatin and hydrolyze starch. The indole test was negative and the VP test was positive. These bacteria can use malonate and citrate to reduce nitrate, which is consistent with the characteristics of Pantoea in Berger's Bacterial Identification Manual19.
Table 3 Physiological and biochemical features of isolates GL9-2 and GL9-3
Test
|
Stain GL9-2
|
Stain GL9-3
|
Gram
|
-
|
-
|
Salt-tolerance
|
+
|
+
|
VP Test
|
+
|
+
|
Malonate utilization
|
+
|
+
|
Cysteine produce H2S
|
+
|
+
|
Xylose
|
+
|
+
|
Inositol
|
+
|
+
|
Mannitol
|
+
|
+
|
Maltose
|
+
|
+
|
Gelatin liquefaction
|
+
|
+
|
Amylolysis
|
+
|
+
|
Lactose
|
+
|
+
|
Nitrate reduction
|
+
|
+
|
Indole test
|
-
|
-
|
Aerobism
|
±
|
±
|
PAL
|
+
|
+
|
Methyl red test
|
+
|
+
|
Citrate
|
+
|
+
|
Note: Symbol of “+”represents positive response, “-”represents negative response.
Molecular biological identification of pathogenic bacteria
16S rDNA sequence analysis
The bacterial 16S rDNA primers 27F and 1492R were used to amplify the total DNA of the pathogen, and a 1350 bp band was obtained. After sequencing, the data were uploaded to NCBI for BLAST comparison. The similarity between the bacterial 16S rDNA and P. agglomerans was 99.4%. The phylogenetic tree constructed based on the 16S rDNA sequence (Fig. 5-a) showed that the isolated strains GL9-2 and GL9-3 had the closest genetic relationship with the P. agglomerans SSH strain (accession No. kt075196.1).
MLSA
The housekeeping genes fusA, gyrB, leus, pyrG, rpoB and rlpB were used to construct multiple genes phylogenetic tree20(Fig. 5-b). The multisite sequences phylogenetic tree of GL9-2 and GL9-3 and 15 Pantoea strains were constructed by MEGA-X software. The phylogenetic results based on multigene sequences showed that the tested strains clustered with the published P. agglomerans strain to form a group and had the highest homology with the P. agglomerans th81 strain.
Whole-genome sequence analysis
Genome-wide basic information
The whole genome of strain GL9-2 was sequenced using the Illumina NovaSeq pe150 sequencing platform, and 1,644,858,704 bp original data were obtained, of which the filtered effective data were 1,629,960,143 bp, accounting for 99.1% of the original data (Table 4). The Q20 of the effective data reached 98.2%, the Q30 reached 94.6%, the predicted genome size was 4,765,392 bp, the G+C content was 55.27%, and the genome was composed of one chromosome and two plasmids. The prediction results of coding genes showed that 4353 CDs were predicted, with a total length of 4,065,777 bp and an average length of 934.02 bp. The CDs accounted for 85.32% of the whole-genome sequence (Table 5).
Table 4 The results of assembly quality control.
Sample Name
|
GL9_2
|
Raw Bases (bp)
|
1644858704
|
Clean Bases (bp)
|
1629960143
|
Clean Q20(%)
|
98.2
|
Clean Q30(%)
|
94.58
|
Total Reads No.
|
191099
|
Total Bases (bp)
|
1516908664
|
Largest (bp)
|
196720
|
Average Len (bp)
|
7937.82
|
Table 5 Genome annotation results
Sample Name
|
GL9-2
|
Chromosome No.
|
1
|
Plasmid No.
|
2
|
Genome Size (bp)
|
4765392
|
G+C(%)
|
55.27
|
Depth
|
318.32
|
COD gene(bp)
|
4353
|
tRNA
|
76
|
rRNA
|
22
|
Genome assembly
The bacterial genome scanning map uses the short sequence assembly software soap denovo2 to splice multiple KMER parameters of the optimized sequence after second-generation sequencing to obtain the optimal contigs assembly results, compare the reads to the contigs, and locally assemble and optimize the assembly results according to the paired end and overlap relationship of reads to form scaffolds.
The bacterial genome completion map assembles three generations of sequences by using assembly software CANU and SPAdes(Fig. 6). If there was an overlap region of more than a certain length at both ends of the final assembly sequence, the sequence was looped, and the overlap sequence at one end was truncated. Finally, complete chromosome and plasmid sequences were obtained.
Gene function annotation
GO function annotation: The protein sequence of the predicted gene was compared with the GO database by BLASTP to obtain the GO annotation information, and blast2GO software was used for GO function cluster analysis. GO annotation includes three aspects: biological processes, cellular components and molecular functions (Fig. 7). According to the classification of cell components, the coding genes were enriched into 14 items, and the number of genes enriched by integral component of membrane and cytotasm was highest. In the biological process category, the items with the largest number of enriched genes were related to regulation of transcription and transmembrane transport. The two items with the largest number of enriched genes in the molecular function category was binding and catalytic activity.
COGs annotation: Coding genes can be divided into 23 categories according to their functions in the COG database. The four entries with the largest number of enriched genes were amino acid transport and metabolism, transcription, carbohydrate transport and metabolism, and inorganic ion transport and metabolism (Fig. 8).
KEGG annotation: The CDs identified to be P. agglomerans were enriched in multiple KEGG entries. In the category of cellular processes, the two items with the largest number of enriched genes were cellular community prokaryotes and cell mobility. In the environmental information processing category, membrane transport and signal transmission pathways were enriched. In the category of genetic information processing, the coding genes were enriched in four pathways, and that with the largest number of enriched genes was translation. In the category of human diseases, the coding genes were enriched in 11 pathways, and the two pathways with the largest number of genes were drug resistance (antimicrobial) and infectious diseases (bacterial). CDs were enriched in the category of metabolism, with 12 entries. The largest number of enriched genes was in global and overview map pathways, followed by carbohydrate metabolism and amino acid metabolism pathways. In the classification of organic systems, the coding genes were enriched into 7 entries, including the endocrine system, aging, environmental adaptation and the immune system (Fig. 9).
PHI annotation: The pathogens in the PHI database are mainly fungi, oomycetes and bacteria. The infected hosts include animals, plants, fungi and insects. The number of matched genes of P. agglomerans coding genes in the PHI database was counted. The results (Table 6) showed that a total of 1131 genes were identified in the PHI database, of which 123 genes were distributed in the increased virulence (hypervirulence) classification, 778 genes in the reduced virulence classification and 18 genes in the lethal classification.
Table 6 Distribution of phenotypic categories of GL9-2 gene orthologs using the PHI database
Phenotype
|
Gene No.
|
reduced virulence
|
778
|
unaffected pathogenicity
|
351
|
increased virulence (hypervirulence)
|
123
|
loss of pathogenicity
|
89
|
effector (plant avirulence determinant)
|
20
|
lethal
|
18
|
chemistry target: sensitivity to chemical
|
2
|
chemistry target: resistance to chemical
|
2
|
Metabolic system analysis: Carbohydrates play an important role in many biological functions. Much meaningful biological information can be obtained by studying carbohydrate-related enzymes. Carbohydrate-active enzymes (cazymes) are a large class of very important enzymes that are divided into glycoside hydrolases (GHs), glycosyltransferases (GTs), polysaccharide lyases (PLs) and glycoesterases. These enzymes have the functions of degrading, modifying and generating glycosidic bonds. In-depth analysis of the types and quantities of cazymes of pathogenic bacteria is of great significance to reveal the pathogenic mechanism of pathogenic bacteria .
CAZY is a professional database on enzymes that synthesize or decompose complex carbohydrates and glycoconjugates. According to the similarity of amino acid sequences in protein domains, cazymes from different species can be divided into GHs, GTs, PLs, carbohydrate esterases (CEs), carbohydrate binding modules (CBMs), and auxiliary oxidoreductases (AAs)(Table 7).
Table 7 annotation of CAZyme
Class Definition
|
Genes No.
|
Precent%
|
Glycoside Hydrolases
|
52
|
39.10%
|
Glycosyl Transferases
|
46
|
34.59%
|
Carbohydrate Esterases
|
20
|
15.04%
|
Auxiliary Activities
|
13
|
9.77%
|
Carbohydrate-Binding Modules
|
2
|
1.5%
|
Secretory system analysis: At present, there are 7 kinds of secretory systems, namely, types I ~ VII. Pathogenic bacteria control the immune response and cell death by secreting TNSS effector proteins into extracellular or host cells. Among them, the type III secretory system (T3SS) is usually used to study the infection mechanism and virulence of gram-negative bacteria at the molecular level. The coding genes of P. agglomerans GL9-2 was annotated into the T1SS, T2SS and T6SS, the Tat secretory system and SEC SRP secretory system, of which the T6SS had the largest number of genes (Table 8).
Table 8 Secretion system type No.of Stain GL9-2
Type
|
Gene No.
|
Type I Secretion System
|
2
|
Type II Secretion System
|
1
|
Type VI Secretion System
|
19
|
Tat Secretion System
|
4
|
Sec-SRP Secretion System
|
12
|