Comparative transcriptome analysis reveals significant differences in gene expression between pathogens of apple Glomerella leaf spot and apple bitter rot

doi:10.21203/rs.3.rs-800525/v1

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Research Article

Comparative transcriptome analysis reveals significant differences in gene expression between pathogens of apple Glomerella leaf spot and apple bitter rot

https://doi.org/10.21203/rs.3.rs-800525/v1

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published 31 Mar, 2022

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Background: Apple Glomerella leaf spot (GLS) and apple bitter rot (ABR) are two devastating foliar and fruit diseases on apple. The different symptoms of GLS and ABR could be related to different transcriptome patterns. Thus, the objectives of this study were to compare the transcriptome profiles of Colletotrichum gloeosporioides, the common pathogen of GLS and ABR, and to evaluate the genes involvement on pathogenicity.

Results: A relatively large difference was discovered between the GLS- and ABR-isolate, and quite a number of differential expression genes associated with pathogenicity were revealed. The DEGs between the GLS- and ABR-isolate were significantly enriched in GO terms of secondary metabolites, however the categories of degradation of various cell wall components did not. A number of genes associate with secondary metabolism were revealed. A total of 17 Cytochrome P450s (CYP), 11 of which were up-regulated while six were down-regulated, and five up-regulated methyltransferase genes were discovered. The genes associated with secretion of extracellular enzymes and melanin accumulation were up-regulated. Four genes associated with degradation of host cell wall, three genes involved in degradation of cellulose, and one gene involved in degradation of xylan were revealed and all up-regulated. In addition, genes involved in melanin synthesis, such as tyrosinase and glucosyltransferase, were highly up-regulated.

Conclusions: The penetration ability, pathogenicity of GLS-isolate was greater than that ABR-isolate, which might be indicate that GLS-isolate originated from ABR-isolates by mutation. These results contributed to highlight the importance to investigate such DEGs between GLS- and ABR-isolate in depth.

Epigenetics & Genomics

Apple Glomerella leaf spot

Apple bitter rot

Colletotrichum gloeosporioides

Transcriptome

Secondary metabolism

Extracellular enzymes

Apple Glomerella leaf spot (GLS) and apple bitter rot (ABR) are two devastating foliar and fruit diseases of apple, which are serious threat to apple production worldwide [1–3]. GLS is an epidemic disease leading to early defoliation and small sunken lesions (1 to 3mm) that rarely develop into a rot on fruit [1, 4]. Apple cultivars descending from the Golden Delicious group, especially the cultivar Gala, are susceptible to GLS, and the disease may cause more than 80% defoliation and diseased fruit before harvest, reducing productivity in subsequent seasons [1, 5]. Nevertheless, apple cultivars from Red Delicious, such as the cultivar Fuji, are highly resistant to GLS [6]. GLS was first reported in Brazil, and is one of the hazardous diseases in apple orchards at present [3, 7]. In China, GLS was first reported in 2011 at Fengxian in Jiangsu province and has become a widespread disease in apple producing areas, such as Shangdong, Hebei, Liaoning and Gansu Provinces [1, 8]. ABR is an ordinary fruit rotting disease of apple that occurs worldwide causing extensive fruit rot [9]. About 30–60% fruits rot before harvest while serious occurring in commercial orchards [10]. All apple cultivars have been considered susceptible to ABR, especially those belonging to the late-harvest group, such as Cripps Pink and Granny Smith, are particularly susceptible [11], whereas few reports on the disease infecting apple leaves.

The causal agent of GLS were identified as Glomerella cingulata (anamorph: Colletotrichum gloeosporioides) [12, 13]. Nevertheless, more Colletotrichum species were described as pathogens since GLS is vigorously occurring in apple-producing regions over the years in several countries [14]. C. fructicola is a main pathogenic species causing GLS worldwide [14]. To date, a total of nine species have been identified as pathogens of GLS, including C. fructicola, C. aenigma, C. fioriniae [15, 16], C. gloeosporioides, C. alienum, C. siamense, C. tropicale, C. karstii, and C. nymphaeae [4, 14], six Colletotrichum species of which belong to in the C. gloeosporioides species complex according the current systematics of Colletotrichum [17]. While C. karstii belong to the C. boninense species complex [18], and both C. fioriniae and C. nymphaeae fall within the C. acutatum species complex [14, 19].

Till now, the causal agent of ABR have been identified as seven Colletotrichum species, including C. fructicola, C. gloeosporioides, C. alienum, C. nymphaeae, C. siamense and C. orientalis [2, 5, 9, 20]. Four Colletotrichum species, such as C. fructicola, C. gloeosporioides, C. alienum, and C. siamense, are common causal agents of GLS and ABR, whereas the pathogenicity of those Colletotrichum species were various on apple. For example, C. fructicola was identified as GLS type and ABR type according to its pathogenicity on apple [2].

Melanized appressoria formed by Colletotrichum species serves as the invasive structure into the host [21]. Appressoria produce a penetration peg that penetrates the plant cuticle and cell wall layers [21]. Appressorial melanization is a key process for successful invading, and three genes PKS1, SCD1, and THR1 associated with the melanin biosynthesis and one regulatory gene CMR1 play crucial role on the penetration [22–25]. Mutants in which the genes encoding these enzymes were disrupted were defective in appressorial melanization and their abilities to penetrate host plants [21].

Pathogens of ABR or GLS exhibit a particular organ specialization being able to cause quite different symptoms [2, 10, 26]. However, the mechanisms for such difference are unclear until know although several reports tried to make clear the relations between GLS- and ABR-isolates. The genotypes causing GLS were may originated from ABR-isolates under the selection force of susceptible apple Gala [27]. The difference ability in infect fruits and leaves could because of extracellular enzymes produced by the fungi, which degrade the plant cuticle and cell wall components while invading [26]. The extracellular enzymes produced by ABR-isolate, such as lipolytic enzymes, proteolytic enzymes, pectin lyase (PNL), polygalacturonase (PG) and laccase (LAC) had higher activity than that produced by GLS-isolate in vivtro [26]. However, those differences in extracellular enzymes of ABR- and GLS-isolate have not been observed in vivo [26].

In this context, the transcriptome profile comparison of GLS- and ABR-isolates were conducted through RNA-SEq. GLS-isolate GC20190701 and ABR-isolates FL180903, FL180906 were showed a basal gene expression pattern. The transcriptional variations among the three isolates were further identified. In addition, the potential roles for the identified DEGs between GLS- and ABR-isolates were analyzed. These results will contribute to a better understanding of the mechanism of difference between GLS- and ABR-isolates and provide insight into the relations of GLS- and ABR-isolates.

The pathogenicity of Colletotrichum isolates GC20190701, FL180903, FL180906 were diverse to Gala apple

The GC20190701 was isolated from leaves of Gala apple, which showed typical symptom of apple Glomerella leaf spot, and the FL180903, FL180906 were isolated from Fuji apple fruits that showed typical symptom of apple bitter rot. The pathogenicity of the three isolates were diverse on apple leaves and fruits. The conidia of GC20190701 could infect Gala leaves, and cause necrotic spots, but the conidia of isolates FL180903 and FL180906 could not complete the infection on Gala leaves without a wound (Fig. 1A). Whereas, the three isolates could infect Gala apple leaves by the conidia thorough micro-wound (Fig. 1B), the necrotic spot caused by GC20190701 and FL180906 were similar in size, which were larger significantly than that caused by FL180903. The three isolates could infect apple fruit through micro-wound by conidia, and cause rot lesions (Fig. 1C). The rot lesions caused by GC20190701 were larger than that caused by FL180903 or FL180906, while rot lesions were similar in size caused by FL180903 and FL180906.

Transcriptome analysis of Colletotrichum isolates GC20190701, FL180903, FL180906

To comprehend the mechanism of pathogenicity of the three Colletotrichum isolates GC20190701, FL180903 and FL180906, transcriptome comparison of three Colletotrichum isolates were performed. The mycelia were harvested from PDA medium at 96 hpi, and RNA were extracted and purified for transcriptome sequence. A total of nine biological samples, three biological repeats for each isolate were sequenced using next generation sequencing on illumina sequence platform. Totally 56.58 billion bp clean data was achieved, which were further spliced to 38,844 assembled unigenes (Table 1, Table S1). The total length of unigenes were 51,182,491 bp, the average length was 1,318 bp, and longest one was 23,113 bp (Table 1).

Table 1

Summary of assembled sequences in the isolates GC20190701, FL180903 and FL180906
Index	Contig	Transcript	Unigene
Total Length (bp)	70,412,489	204,569,566	51,182,491
Sequence Number	182,409	96,684	38,844
Max. Length (bp)	28,478	23,113	23,113
Mean Length (bp)	386.0	2,115.9	1,317.6
N50 (bp)	1,670	3,718	3,061
N50 Sequence No.	9,219	18,204	5,019
N90 (bp)	134	1,176	439
N90 Sequence No.	120,590	53,845	20,756
GC%	52.7	54.9	53.8

Functional annotation and classification of unigenes

Gene annotation was performed to analyze the functions of the expressed genes. Totally 24,783 (63.80% of 38,844) unigenes were annotated in at least one database. A total of 23,089 unigenes (59.44% of 38,844) were annotated in NCBI non-redundant protein sequences (NR), and 14,445 unigenes (37.19%), 7,662 unigenes (19.73%), 10,049 unigenes (19.73%), 19,696 unigenes (50.71%), 15,372 unigenes (39.57%), were annotated in GO, KEGG, Pfam, eggNOG, and Swiss-Prot, respectively (Table S2). There were 3,655 unigenes (9.41%) were annotated in all the databases (Table S2).

The annotated unigenes were compared to known nucleotide sequences of microbe species. The best matched to the known nucleotide sequences were C. gloeosporioide CG-14 (42.53%) and C. gloeosporioide Nara gc5 (29.28%). Only 5.86% unigenes matched to others four Colletotrichum species (Fig. 2A). Therefore, the three isolates were identified as C. gloeosporioide.

Clusters of Gene Ontology (GO) classification were calculated by BLAST2GO. A total of 14,445 annotated unigenes (37.19% of 38,844) were assigned into at least one of the 48 GO terms (Fig. 2B, Table S3). The unigenes were assigned to biological process, cellular component, and molecular function, respectively. The unigenes in the molecular function category were abundant in catalytic activity (GO:0003824) and binding functions (GO:0005488) (Fig. 2B). The biological process category of the unigenes were predominantly associated with metabolic process (GO:0008152), single-organism process (GO:0044699) and cellular process (GO:0009987) (Fig. 2B). The cellular component category of the unigenes were predominantly associated with cell (GO:0005623), cell part (GO:0044464) and membrane (GO:0016020) (Fig. 2B). The fundamental biological processes of the isolates GC20190701, FL180903, FL180906 were identified according to the above findings.

Biological functions of annotated unigenes in different pathways were systematically evaluated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. A total of 7,662 annotated unigenes were matched to the KEGG database, which were assigned to 33 KEGG pathways (Fig. 2C, Table S4,). Metabolism pathways (4,776 unigenes, 48.8%), genetic information processing (1,937 unigenes, 19.8%) and organismal systems pathways (1,446 unigenes, 14.8%) were the three dominant categories. The subcategory translation pathway in genetic information processing (990 unigenes), carbohydrate metabolism (954 unigenes), and amino acid metabolism (738 unigenes) in metabolism pathways were the three dominant subcategories (Fig. 2C, Table S4,).

Differential expression analysis of unigenes of isolates GC20190701, FL180903, FL180906

In order to analyze the differential expression of unigenes (DEGs) among the three isolates, the transcriptomes were compared in pairs, and the expression level of unigenes were calculated by the FPKM method. In the isolate GC20190701, a total of 8,302 unigenes were differential expressed compared to the isolate FL180906, and the up-regulated unigenes and down-regulated unigenes were 4,788 and 3,514, respectively (Fig. 3A). There were 9,455 DEGs between the isolate GC20190701 and FL180903, and the up-regulated genes and down-regulated genes were 5,185, and 3,640 compared to FL180903 (Fig. 3A). Whereas only a total of 1,115 DEGs between the isolate FL180906 and FL180903, the up-regulated unigenes were 378, and the down-regulated unigenes were 737 compared with FL180906 (Fig. 3A).

A total of 145 up-regulated unigenes and 447 down-regulated unigenes overlapped between GC20190701 and FL180903 compared with FL180906 (Fig. 3B). The transcriptome expression profile of GC20190701 was quite difference from that isolates FL180903 and FL180906, whereas the transcriptome expression profile of FL180903 was not that different from that FL180906.

Gene ontology and KEGG enrichment analysis of DEGs of isolates GC20190701, FL180903, FL180906

Gene ontology (GO) enrichment analysis were conducted using a cut-off of P < 0.05 to determine the functional roles of DEGs of the three isolates. The DEGs of isolate GC20190701 and FL180906 were assigned in 3,565 enriched GO terms. There were 184 significant enriched GO terms (p < 0.05), which the numbers related with biological process, molecular function and cellar component were 89, 81 and 14, respectively (Table S5). The significantly enriched GO categories of DEGs of isolate GC20190701 and FL180906 included those involved in biosynthesis of secondary metabolites, including oxidation-reduction process (GO:0055114), methylation (GO:0032259), methyltransferase activity (GO:0008168), oxidoreductase activity (GO:0016491), heme binding (GO:0020037), monooxygenase activity (GO:0004497), oxidoreductase activity (GO:0016705), transmembrane transport (GO:0055085), and integral component of membrane (GO:0016021) (Fig. 4A, Table S5). However, the categories of degradation of various cell wall components (including catabolism of cellulose (GO:0030245), xylan (GO:0045493), pectin (GO:0045490)), peptidase activity (GO:0008233), fatty acid metabolic processes (GO:0006631), and binding (GO:0005488) activities were not significantly enriched (P > 0.05) (Fig. 4A, Table S5).

The DEGs of isolate FL180903 and FL180906 were assigned in 909 enriched GO terms. Totally 109 significant enriched GO terms (p < 0.05) were identified, the numbers related with biological process, molecular function and cellar component were 43, 61 and 5, respectively (Table S6).

KEGG enrichment analyses were performed to identify the basal level biological pathways of the three isolates. All DEGs between GC20190701 and FL180906 were enriched into 110 KEGG pathways (Table S7). A total of 14 pathways were significantly enriched with P values < = 0.05 (Fig. 4B, Table S7). The DEGs between FL180903 and FL180906 were enriched into 60 KEGG pathways, and eight of which were significantly enriched (Table S8).

Functional analysis of the genes that differentially expressed in GLS-pathogen and ABR-pathogen

To compare the function of DEGs of GLS- and ABR-pathogen, we firstly removed the DEGs from GC20190701 which were co-differential expressed with FL180903 compared with the expression of FL180906 since the FL180903 and FL180906 were common pathogens of ABR. Second, we selected the highly differential expression genes with a |log2FoldChange| > 3, the p value < 0.01, and the adjust p value < 0.01 from the transcriptome of GC20190701. A total of 1,124 DEGs were selected, and 649 genes were up-regulated, 475 genes were down-regulated compared with the expression of FL180906 (Table S9).

The functions of the 1,124 DEGs were analyzed by searching the Swissprot database. Among the DEGs, 42 unigenes associated with the biosynthesis of secondary metabolites including cytotoxin, mycotoxin, and antibiotics were revealed, of which 30 unigenes were up-regulated whereas 12 were down-regulated (Table 2). There were 17 cytochrome P450, five methyltransferase protein, three short-chain dehydrogenase reductase, three FAD binding protein, two multicopper oxidase, two efflux pump antibiotic resistance, two NmrA-like family protein were involved in the 42 DEGs, besides that one of each Mgt family protein, ABC-2 type transporter, 4-hydroxyacetophenone monooxygenase, Aminotransferase classes I and II family protein, dynamin GTPase, integral membrane protein, oxidoreductase protein, thioredoxin were also included in the 42 DEGs (Table 2).

Table 2

The selected highly differently expression genes in GLS-isolates compared with ABR-isolate associated with the biosynthesis of secondary metabolites and pathogenicity
Gene_id	log2^FoldChange	p value	p adj	Length	NR description
DN11786_c0_g1	10.1726	1.87481E-17	1.35521E-15	2229	Cytochrome P450
DN13770_c0_g1	7.7964	7.73587E-12	2.28663E-10	1861	Cytochrome P450
DN20588_c0_g1	6.4786	6.2621E-08	9.9134E-07	2105	Cytochrome P450
DN32057_c0_g1	5.7993	6.67483E-12	1.98619E-10	2533	Cytochrome p450 pisatin
DN29395_c0_g1	5.2268	1.57697E-10	3.86549E-09	1981	Cytochrome p450 family protein
DN28542_c0_g1	5.0673	1.34696E-09	2.79951E-08	1931	Cytochrome P450
DN35922_c0_g1	4.5778	7.70861E-09	1.41189E-07	5435	Cytochrome P450
DN28908_c0_g1	3.9370	0.001122151	0.007588586	2120	Cytochrome p450 oxidoreductase
DN30184_c0_g1	3.7122	1.71709E-06	2.10697E-05	2691	Cytochrome p450 family protein
DN33241_c0_g1	3.6714	1.62331E-06	2.00374E-05	2984	Benzoate 4-monooxygenase cytochrome p450
DN28885_c0_g1	3.0654	0.000521922	0.003845902	1748	Cytochrome P450
DN28563_c0_g1	-3.3410	0.000871754	0.006063741	2597	Cytochrome P450
DN33475_c0_g1	-3.4703	4.16395E-06	4.78347E-05	5798	Cytochrome P450
DN32532_c1_g1	-3.8683	0.000716447	0.005092608	2717	Cytochrome P450
DN28222_c0_g1	-5.0447	0.000139771	0.00117946	2290	Cytochrome P450
DN28049_c0_g2	-5.4077	6.91285E-09	1.27362E-07	2119	Cytochrome P450
DN33453_c0_g1	-5.6123	8.05501E-11	2.05531E-09	2624	Cytochrome P450
DN31973_c0_g3	3.7649	5.08745E-06	5.77806E-05	1320	Methyltransferase domain-containing protein
DN31973_c0_g2	8.8234	4.8846E-17	3.32997E-15	4825	Methyltransferase domain-containing protein
DN31000_c2_g2	5.6164	1.58532E-10	3.88292E-09	1677	Methyltransferase domain-containing protein
DN30174_c1_g1	4.2519	7.75456E-07	1.01852E-05	1595	O-methyltransferase
DN31843_c0_g1	5.6821	1.30938E-05	0.000136617	1218	SAM dependent methyltransferase, putative
DN34847_c0_g2	3.1430	0.000442733	0.003313322	1568	Short-chain dehydrogenase reductase
DN28087_c0_g1	4.8386	6.61206E-06	7.34398E-05	1359	Short-chain dehydrogenase reductase
DN28633_c0_g2	6.3343	3.4573E-09	6.71105E-08	2386	Short-chain dehydrogenase reductase family
DN31745_c1_g2	-8.6067	6.59755E-07	8.80663E-06	4113	FAD binding domain-containing protein
DN29628_c0_g1	3.8020	3.77962E-06	4.37747E-05	2911	FAD binding domain-containing protein
DN27472_c0_g1	3.2978	0.000250727	0.001990167	1652	FAD binding domain-containing protein
DN36036_c8_g4	4.4081	1.53968E-08	2.69184E-07	416	Multicopper oxidase
DN36036_c7_g1	6.9116	1.84547E-09	3.75298E-08	212	Multicopper oxidase
DN35008_c0_g1	-4.9712	0.000773001	0.005452308	1069	Efflux pump antibiotic resistance
DN36131_c2_g2	8.3251	1.12354E-18	1.00586E-16	2959	Efflux pump antibiotic resistance
DN24662_c0_g1	-11.5043	2.41435E-05	0.000239094	1055	NmrA-like family protein
DN23599_c0_g1	-4.2513	1.39353E-05	0.000144477	1143	NmrA-like family protein
DN22384_c0_g1	4.1657	0.001295912	0.00860974	1331	4-hydroxyacetophenone monooxygenase
DN28500_c0_g1	5.7415	3.74839E-09	7.23103E-08	1430	Aminotransferase classes I and II family protein
DN29598_c0_g1	7.9391	5.26601E-15	2.51244E-13	4129	Dynamin GTPase
DN35303_c1_g1	11.6073	4.60773E-24	1.49535E-21	1116	Integral membrane protein
DN28459_c0_g3	3.4975	0.000907408	0.006279499	533	Oxidoreductase family protein
DN30510_c0_g5	10.9541	2.24851E-21	3.70648E-19	533	Thioredoxin
DN20622_c0_g1	-5.6331	3.54404E-06	4.12456E-05	1468	MGT family
DN32373_c0_g2	-3.8836	5.84537E-06	6.56498E-05	4913	ABC-2 type transporter
DN36403_c6_g2	4.2504	6.06648E-08	9.65771E-07	4007	CFEM domain-containing protein
DN21054_c0_g1	4.4493	0.000368273	0.002816287	1726	CFEM domain-containing protein
DN27480_c0_g1	-13.0590	1.68911E-08	2.93171E-07	4310	CFEM domain-containing protein
DN26894_c0_g1	4.4476	5.68732E-06	6.399E-05	1644	Rhamnogalacturonate lyase
DN21088_c0_g1	3.7560	1.04166E-05	0.000110647	1791	Beta-glucosidase
DN33240_c0_g2	6.2602	1.4174E-06	1.77203E-05	2190	Tyrosinase 2
DN34063_c0_g2	6.3368	7.18132E-12	2.1308E-10	3030	Tyrosinase precursor
DN30086_c0_g1	3.3697	2.0996E-05	0.000210196	1749	PHB depolymerase family esterase
DN32627_c0_g1	3.2690	1.9454E-05	0.000195765	3296	Endoglucanase III
DN36348_c7_g7	6.0139	6.48164E-07	8.6742E-06	578	Serine carboxypeptidase
DN34219_c0_g1	3.3009	1.30281E-05	0.000136023	2526	Carboxypeptidase s1
DN28578_c1_g1	5.6735	2.13051E-08	3.62909E-07	1939	Carboxypeptidase 2

Four genes associated with degradation of host cell wall were identified, which were up-regulated. One Rhamnogalacturonate lyase (DN26894_c0_g1), two glucosidases (DN21088_c0_g1, DN32627_c0_g1), involve in degradation of cellulose, and one acetylxylan esterase (DN30086_c0_g1) involves in degradation of xylan.

Two tyrosinase (DN33240_c0_g2, DN34063_c0_g2), and one glucosyltransferase (DN25823_c0_g1) were up regulated, which were involved in melanin synthesis. Three CFEM domain-containing protein, which were associated with pathogenicity were also identified. Two of them (DN36403_c6_g2, DN21054_c0_g1) were up-regulated, and one (DN27480_c0_g1) were down-regulated. Three carboxypeptidases (DN36348_c7_g7, DN34219_c0_g1, DN28578_c1_g1) were identified, which were associated with pathogenicity, and both of them were up regulated.

Validation of RNA-Seq Data by Quantitative Real-Time RT-PCR

We validated the RNA-Seq data by quantitative RT-PCR for six representative genes that showed strong up-regulation or down-regulation in GC2190701 compared with FL180906. The genes used for validation, their log2 fold change, and the primer sequences are presented in Table S10. For quantitative RT-PCR, we prepared new samples following the same procedures that were used to prepare samples for RNA-SEq. The expression patterns of the selected six genes all agreed with the RNA-Seq results (Fig. 5), suggesting that the RNA-Seq results were reliable in this study.

Apple Glomerella leaf spot (GLS) and apple bitter rot (ABR) are two serious plant disease that threaten to the production of apple worldwide. The causal agents of GLS and ABR both belong to the genus Colletotrichum, and there are several common species causing both GLS and ABR. However, the common causal agents exhibit a particular organ specialization, which cause quite different symptoms on apple. Although several reports tried to explore the mechanism for such difference [2, 26, 27], causes for such differences remain unknown until now. In context, we performed transcriptome comparation of three GLS- and ABR-isolates, GC20190701, FL180903 and FL180903. A relatively large difference was discovered between the GLS- and ABR-isolate, and quite a number of differences expression genes associated with pathogenicity were revealed.

Totally 71.81% transcripts were assigned to C. gloeosporioide by comparing to known nucleotide sequences of microbe species (Fig. 1A), therefore, the three isolates were identified as C. gloeosporioide. C. gloeosporioide was one of the common pathogen of GLS and ABR [5, 28], which belonged to the C. gloeosporioide Complex [17]. Although belonging to same Colletotrichum species, the transcriptome expression patterns were divergent in the three isolates GC20190701, FL180903 and FL180906. A total of 1,115 unigenes were differently expressed between FL180903 and FL180906, both of which cause ABR on fruit (Fig. 3A). Nevertheless, the number of DEGs were 9,455 and 8,302 in GC20190701 compared with FL180903 and FL180906, respectively (Fig. 3A). As causal agent of GLS and ABR, the isolates not only cause the various symptoms but also possess significant differences in the transcriptome profiles.

The enrichment of GO terms and KEGG analysis of DEGs were conducted. The DEGs between the GC20190701 and FL180906 were significantly enriched in secondary metabolites, however the categories of degradation of various cell wall components did not significantly enrich. The secondary metabolites were significantly divergent between the GLS-isolate and ABR-isolate, while the degradation enzyme may not change that much. However, the enrichment of GO terms of DEGs between isolate FL180903 and FL180906 had the identical trends with that DEGs between GC20190701 and FL180906 although the number of unigenes involved in the GO terms were less than that of GC20190701 and FL180906.

We combined several measures in the analysis procedure to reveal rigorous DEGs between GLS-isolate and ABR-isolate. First, we removed the DEGs common in GC20190701 and FL180903 comparing with FL180906 because we considered that the common DEGs were not the causes of different symptom since both the isolates FL180903 and FL180906 cause ABR on fruit. Second, only DEGs showing more than 8-fold differences, the p value < 0.01, and the adjust p value < 0.01 were kept for further functional analysis. A total of 1,124 DEGs were selected, which were consider as the DEGs of GLS- and ABR-isolate (Table S9). Looking through the DEGs, secondary metabolites, such as cytotoxin, mycotoxin, and antibiotics, were various between GC20190701 GLS- and ABR-isolate while extracellular enzymes were mostly up- regulated.

Killing the host tissues using the produced toxins, such as sesquiterpenoids are the main strategies for fully pathogenicity of necrotrophic fungal pathogens [29]. Several putative sesquiterpene synthases (STS) were revealed during plant infection in C. graminicola and C. higginsianum [30]. Cytochrome P450s (CYP) play essential roles in the fungal biosynthesis of secondary metabolites and detoxification of toxic compounds [29, 31, 32]. In this study, a total of 17 Cytochrome P450s (CYP) were revealed in the DEGs, 11 of which were up-regulated while six were down-regulated, which demonstrated that the secondary metabolites might be diversiform between GLS- and ABR-isolate. The functional roles of each CYP in the GLS- and ABR-isolate should be further explored. The methyltransferases catalyze an important pathway in the metabolism of many drugs and toxic compounds [33]. In the maize pathogen Cochliobolus heterostrophus, the Lae1-like methyltransferases acts as a regulator of T-toxin production and thus impacts virulence to the host [34]. Five Methyltransferase protein were revealed from the 1,124 DEGs, which were all up-regulated. Those methyltransferases may involve in the pathways that increased the toxin level in GLS-isolate, and therefore results in large necrotic spots and premature defoliation on leaves.

As hemibiotrophic fungi, C. gloeosporioide infection procedure include penetration, growth inside living host cells (biotrophy) and tissue destruction (necrotrophy) [30]. To complete the pre-penetration, an invasion structures having mechanical pressure and some cuticle- and cell wall degrading enzymes, such as, cutinases, pectinases, hemicellulases and cellulases are necessary [35]. Besides that, many enzymes, such as amylases, lipases and proteases are also secreted to degrade plasma-membrane components, and provide nutrients to help fungus spread in plant tissue [36]. We retrieved four genes associated with degradation of host cell wall, which were up-regulated in GLS-isolate compared with ABR-isolate. Three genes involved in degradation of cellulose, and one gene involved in degradation of xylan (Table 1). Those results demonstrated that GLS-isolate secreted more cuticle- and cell wall degrading enzyme than that ABR-isolate, which might be the reason that GLS-isolate could infect Gala leaves and developed fast but ABR-isolate could not. However, Velho et al. proved that ABR-isolate had higher activity of pectin lyase (PNL), polygalacturonase (PG) and laccase (LAC) than GLS-isolate in culture broth [26]; however, they could not found significant difference between the two isolates for those enzymes in infected apple leaves [26].

C. gloeosporioide penetrates host epidermal cells through the melanized appressoria by producing a penetration peg, and melanization of appressoria is crucial for appressorial function [37, 38]. In current study, we identified genes involved in melanin synthesis, such as tyrosinase and glucosyltransferase, which were highly up-regulated (Table 2). The melanin accumulation level might higher in GLS-isolate than that in ABR-isolate that cause a greater invasive ability on host plant tissues. In addition, we revealed several genes associated with pathogenicity, such as two CFEM domain-containing protein and three carboxypeptidases were up regulated. Combined the above founding, we concluded that the penetration ability, pathogenicity of GLS-isolate was greater than that ABR-isolate.

The transcriptome profile between GLS- and ABR-isolate were relatively large differences, and genes involved in the secondary metabolism and extracellular enzymes were divergent. More and higher secondary metabolites were produced in GLS-isolate, the secretion of extracellular enzymes and melanin accumulation were increased, and the genes associated with pathogenicity were also up-regulated. Therefore, pathogenicity of GLS-isolate was higher than that ABR-isolate, which might be indicate that GLS-isolate originated from ABR-isolates by mutation into a more virulent strain. The consistent deduction was concluded in previous studies [27], and the mutation caused mainly by the high production of susceptible apple Gala acting as selection force [2].

Fungi isolates

GLS-isolate GC20190701 and ABR-isolates FL180903, FL180906 were originated from leaves and fruits with GLS- and ABR symptoms, respectively. The GLS leaves and ABR fruit were collected from Gala and Fuji at the experimental station of Qingdao Agricultural University, Jiaozhou City of China, respectively. The causal agents were isolated from diseased leaves or fruits by single spore isolation method as descripted previous [39]. Fungus was grown on potato dextrose agar (PDA) medium at 25°C in an incubator (MGC-400HPY; Shanghai Bluepard Instruments Co. Ltd., Shanghai, China).

Pathogenicity test assays

Conidia of the three isolates GC20190701, FL180903, FL180906 were inoculated to apple leaves and fruits to test the pathogenicity. The three isolates were incubated on PDA medium for three days at 25°C, subsequently the mycelium was scraped from the surface of PDA medium to induce the production of conidia. The produced conidia by each isolate were scoured into water, and adjust to 1×10⁴ per mL using a hemocytometer. Twenty microliter conidia were dropped to Gala leaves or fruit by a micropipettor, and the inoculated leaves or fruit were sealed in a container, which keep moisture using a wetted tissue. The sealed containers with inoculated leaves and fruits were incubated at 25°C in an incubator, and the symptoms causing by the three isolates were examined at 10 dpi.

RNA extraction, library construction, and sequencing

The three isolates GC20190701, FL180903, FL180906 were conducted transcriptome analysis. A total of nine isolate cultures, three replicas for each isolate, were subjected to RNA extraction. The isolates were incubated on PDA medium for 96 h at 25°C in an incubator, and mycelium were harvested from surface of medium for RNA extraction. Total RNA of the nine samples were extracted using the Trizol Reagent Kit (Sangon Biotech, Shanghai, China) and treated with RNase-free DNase I (TaKaRa) following the manufacturer’s protocols. Promega PolyATtract mRNA Isolation Systems were used to purified poly(A) messenger RNA (mRNA) from the total RNAs, biotinylated beads with oligo (dT) were used to enrich mRNAs. The mRNAs were fragmented into short fragments of about 300bp in length using Magnesium RNA Fragmentation Module (New England BioLabs). Subsequently, using the short fragments as templates, the first-strand cDNA was synthesized applying random hexamer-primers, and then the second-strand cDNA was synthesized by adding the buffer, dNTPs, RNase H, and DNA polymerase I. Next, the short fragments were connected with sequencing adapters with respect to the result of agarose gel electrophoresis, and suitable fragments of about 450bp in length were selected as templates for amplification with PCR to enrich the cDNA fragments. Next, the PCR products were purified with a QIAquick PCR Purification Kit and elution in EB buffer, the obtained products were considered as the final cDNA library for sequencing. Finally, Quality of cDNA libraries were analyzed by Agilent 2100 Bioanalyzer, and the libraries were conducted paired-end sequencing using next-generation sequencing (NGS)on Illumina HiSeq™ 4,000 (Illumina, CA, USA). Preparation and sequencing of the cDNA library were implemented by Shanghai Personalbio Technology Co.,Ltd. (Shanghai, China).

De novo assembly of sequencing reads

To cleaned-up the raw sequencing reads, the following criteria were applied to remove the low-quality sequences: adapter and sequence less than 50bp, and low-quality reads with more than 50% of bases with quality lower than Q20 level. The high-quality clean reads were recovered after filtering, and used for transcriptome de novo assembly with trinity software (v2.8.4). The Trinity software first combined reads with a 30 bp length of overlap to form longer fragments without N, and these N-free assembled reads were assembled to generated transcripts. Subsequently, the generated transcripts were clustered and the longest one of each transcript was considered as an unigene. Reads counts of each unigene were calculated by mapping the transcripts back to unigenes.

Functional annotation and differentially expressed genes

To functional analyze the transcriptomes, the assembled unigenes were firstly aligned by BLASTX to protein databases, such as NR (NCBI non-redundant protein sequences), GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genome), eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups), Swiss-Prot, and Pfam, to retrieve proteins with the highest sequence similarity with the unigenes along with their protein functional annotations. Then, the gene expression level was calculated with RSEM (v1.1.12) using the transcripts as reference sequence database. The clean reads of each sample were aligned to transcripts database, the reads of each transcript were calculated for each sample. To compare the difference of gene expression among different samples, the FPKM (Fragments per kilobase of transcript per million mapped reads) method was used for normalization [40].

The DEGs among the three isolates GC20190701, FL180903, FL180906 were identified by RSEM (RNA-Seq by Expectation-Maximization) software by the following filter criteria: |log2FoldChange| > 1, and p-value ≤ 0.05 [41].

Gene ontology and KEGG pathway enrichment analysis

To examine the biological functions and pathways of the identified DEGs, we firstly annotated the DEGs with GO database (http://www.geneontology.org/) using hypergeometric test [41]. GO terms which are significantly enriched in DEGs compared to the genome background were retrieved by GO functional enrichment analysis. Briefly, the GO functional enrichment analysis firstly maps all DEGs to GO terms in the database, calculating gene numbers for every term, then using ultra-geometric test to find significantly enriched GO terms (P-value < 0.05) in DEGs comparing to the genome background.

The significantly enriched metabolic pathways and signal transduction pathways in DEGs comparing with the whole genome background identified by KEGG pathway enrichment analysis. Using FDR = 0.05 and P-value < 0.05 as the threshold, pathways were defined as those with significant enrichment for DEGs.

Quantitative RT-PCR validation

Total RNA was extracted from the three fungi isolates GC20190701, FL180903, FL180906 as described for the transcriptome library preparation. The purity and concentration of each RNA sample was measured in triplicate using a nanophotometer (Implen GmbH, Germany). cDNA was synthesized using the HiScript III RT SuperMix for qPCR with gDNA wiper (R323-01, Vazyme, China) according to the manufacturer's instructions, and alpha-tubulin (TUB) gene was used as an internal control [42]. The primers for qPCR were designed according to sequences of the chosen unigenes using PrimerQuest Tool (https://sg.idtdna.com/PrimerQuest/Home/Index). Primer sequences and unigenes are summarized in Table S10.

Quantitative PCR (qPCR) was carried out in a LightCycler® 96 System Real-Time PCR System (Roche, USA) using Taq Pro Universal SYBR qPCR Master Mix (Q712-02, Vazyme, China). The thermal cycles were set as follows: 95°C for 30 s, followed by 40 cycles of 95°C for 15 s and 60°C for 30 s. The PCR products were subjected to melt curve analysis to verify the specificity. Three biological replicates and three technical replicates for each biological replicate were performed for each unigene. The Ct value for each biological replicate were calculated as the average value of three technical replicate. The relative expression level of each gene was calculated using the 2^−(ΔΔCt) method [43]. Experiments were conducted in triplicates.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest.
Funding: This research was supported in part by the Natural Science Foundation of Shandong Province (ZR2020MC114); the Talents of High-Level Scientific Research Foundation of Qingdao Agricultural University (6631116026); the National Key Research and Development Program (2021YFE0104200); and the China Agriculture Research System (CARS-28).

Authors’ contributions

Conception and design: B.H.L., S.L.; Acquisition of data, analysis and interpretation: B.J., T.C., X.Y.Y, Y.Y.D., K.X.Y., P.P. Z.; Drafting and revision of the manuscript: P.L.L., C.X.W., N.L., S.L; The authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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Comparative transcriptome analysis reveals significant differences in gene expression between pathogens of apple Glomerella leaf spot and apple bitter rot

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Journal Publication

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Abstract

Figures

Introduction

Results

Functional annotation and classification of unigenes

Differential expression analysis of unigenes of isolates GC20190701, FL180903, FL180906

Gene ontology and KEGG enrichment analysis of DEGs of isolates GC20190701, FL180903, FL180906

Functional analysis of the genes that differentially expressed in GLS-pathogen and ABR-pathogen

Validation of RNA-Seq Data by Quantitative Real-Time RT-PCR

Discussion

Conclusions

Material And Methods

Fungi isolates

Pathogenicity test assays

RNA extraction, library construction, and sequencing

Functional annotation and differentially expressed genes

Gene ontology and KEGG pathway enrichment analysis

Quantitative RT-PCR validation

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

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