Meta-QTL analysis for mining of candidate genes and constitutive gene network development for viral disease resistance in maize (Zea mays L.)

Abstract

Viral diseases severely impact maize yields, with occurrences of maize viruses reported worldwide. Deployment of genetic resistance in a plant breeding program is a sustainable solution to minimize yield loss to viral diseases. The meta-QTL (MQTL) has demonstrated to be a promising approach to pinpoint the most robust QTL(s)/candidate gene(s) in the form of an overlapping or common genomic region identified through leveraging on different research studies that independently report genomic regions significantly associated with the target traits. Here, we employed an MQTL approach by targeting 39 independent research investigations aimed at genetic dissection of the resistance in maize against 14 viral diseases. We could project 27 % (53) of the total 196 QTLs onto the maize genome. Our analysis found a robust set of 14 MQTLs on chromosomes 1, 3 and 10 that explain significant proportion of the variations for resistance against 11 viral diseases. Marker trait associations (MTAs) identified from genome-wide association studies (GWAS) provide evidence in support of the two MQTLs (MQTL3_2 and MQTL10_2) playing crucial roles in viral disease resistance (VDR) in maize. A total of 1,715 candidate genes underlie the identified MQTL regions, of which, we further examined the constitutively-expressed genes for their involvement in various metabolic pathways. The involvement of the identified genes in the antiviral resistance mechanism renders them a valuable genomic resource for allele mining and elucidating plant-virus interactions for maize research and breeding.

1. Introduction

Maize (Zea mays L.) is the most productive cereal crop, with an annual production of 1147.6 billion tonnes across 194 million hectares in 170 countries [1]. It is mainly utilized for poultry feed, animal feed, and starch making followed by its use as a food crop and bioethanol production [2]. Being an important global crop, genomic resources such as genome sequencing and high-density genotyping of large and diverse germplasm are well-developed for maize [3]. Notwithstanding this, several constraints such as abiotic (drought, heat, salinity, waterlogging) and biotic stresses (diseases and insects/pests) hinder its productivity to meet the global demand for maize production. Among these constraints, viral diseases cause a 3 % drop in maize production annually, resulting in a loss of 26 billion tonnes grain worth around 4.5 billion USD [4,5]. The incidence of viral diseases is becoming severe due to the lack of effective control measures, exacerbated by the changing climates. In addition, novel viral diseases have emerged at a fast pace due to the cultivation of crop plants under new environmental conditions (away from their domestication centers) and the continuous practice of monocultures [[6], [7], [8]]. More than 50 virus strains have been identified to cause over 40 viral diseases in maize globally [9]. The typical viral infection symptoms include streaks, mosaics, chlorosis, dwarfing or stunting, and necrosis. Most viral diseases are quantitatively inherited and hence are highly influenced by the environment (E) and genotype-environment interaction (GEI). QTL mapping has enabled the identification of QTLs (quantitative trait loci) underlying viral-disease related traits such as red edges (RE), infection rate (IR), virus extinction (VE); disease severity index (DSI) at seedling, elongation, anthesis, and grain filling stage, the area under disease progress curve (AUDPC), AUDPC over 42 days (AUT), mean scoring of plants presenting symptoms/plot (ANMT), the proportion of symptom-free plants/plot (APIT) [[10], [11], [12], [13], [14]], which serves as a foundational step to undertake marker-assisted breeding (MAB) for accelerated genetic improvement. A growing body of literature on maize pertains to QTL mapping studies for resistance to various viral diseases [[13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]]. However, fine-mapping studies are limited to only a handful of QTLs [15,[29], [30], [31], [32], [33]]. All these studies yielded different outcomes due to various factors like different parental populations (genotyping), size of mapping populations, marker types used and diverse environment backgrounds, etc. This makes it difficult to utilize the information of reported QTLs for comparison across studies and identify consensus or co-located genomic regions for traits of interest. Various QTL mapping, MetaQTL analysis, genomic studies, comparative genome analysis and meta-analysis of expressive genes have been done to identify precise genomic locations for different traits in a range of crop species [[34], [35], [36], [37], [38], [39], [40], [41]] Meta-QTL (MQTL) analysis combines QTL mapping data from several research studies and aids in identifying consensus genomic areas/QTLs shared across genotypes and environments, providing crop researchers with robust genomic candidates for marker-assisted selection (MAS) programs. The MQTLs are the consistent QTL identified by MQTL analysis with a 95 percent confidence interval (CI) using independent studies [42]. The ideal candidate MQTL for MAS are those with strong and consistent influence on the trait of interest, have minimum CI, and form a cluster with several initial QTLs, is known as “MAS-friendly MQTL” [43]. Such genomic regions can be further explored to mine the candidate genes using a reference physical map. Furthermore, the MTAs (marker-trait associations) from GWAS (genome-wide association studies) on VDR (viral disease resistance) can be used to verify the candidate genes linked to specific diseases. Afterward, conducting functional analysis for the identified candidate genes can assist in further narrowing down to specific gene(s) that contribute directly or indirectly to specific traits. The publicly available gene expression data can be mined to explore the expression of putative candidate genes for VDR through gene network analysis. Previous studies on metaQTL analysis of VDR in maize have focussed on a limited number of viral diseases [[44], [45], [46]], warranting a comprehensive metaQTL study to dissect the candidate genes and their networks. In this study, the MQTL analysis was performed using the major QTLs for traits related to 14 major viral diseases to identify MQTL regions across the maize genome. We further investigated candidate genes, constitutive genes and their network within the identified MQTLs implying their role in VDR to use these as a useful resource for VDR breeding programs in maize.

2. Materials and methods

2.1. Review of literature and development of QTL database

A thorough systematic literature review was performed to find studies on major viral diseases in maize, utilizing Web of Science and Google Scholar (Fig. 1). As a result, the current MQTL study included a total of 39 experimental trials encompassing initial QTLs for 14 viral diseases (Table 1). The 14 viral diseases included were Maize streak virus (MSV), Mal de río cuarto virus (MRCV), Sugarcane mosaic virus (SCMV), Maize stripe virus disease (MSD), Barley yellow dwarf virus (BYDV), Foxtail mosaic virus (FoMV), Maize chlorotic dwarf virus (MCDV), Maize rough dwarf virus (MRDV), Maize mosaic virus (MMV), Maize dwarf mosaic virus (MDMV), Maize rayado fino virus (MRFV), Maize lethal necrosis (MLN), Maize fine streak virus (MFSV), Maize chlorotic mottle virus (MCMV). A total of 39 experimental trials from 30 QTL mapping populations were analyzed. For the preparation of QTL files, information on all relevant factors such as name of QTL, linked traits, QTL position, linkage group (chromosome number), confidence interval (CI), LOD values, phenotypic variance explained (R²), and so on (Table S1) were used, omitting studies (21) that lacked such information. In order to prepare map files, the data of genetic map was extracted from either published research studies or the maize genomic database/MaizeGDB (http://www.maizegdb.org/).

Fig. 1.

Flow chart showing reviewed studies and included in the MQTL analysis for VDR From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372:n71. https://doi.org/10.1136/bmj.n71.

Table 1.

List of experiments used from various published studies for meta-QTL analysis for viral diseases in maize.

S.No.	Viral Disease	Parental germplasm	Population Size	Mapping Population	QTL number	Reference
1	MRCV	BLS14 × Mo17	145	F2:6	3	Bonamico et al. (2012)
2	SCMV	L520 × L19	150	F2:3	2	Regina Prazeres De Souza et al. (2008)
3	MRCV	BLS14 × Mo17	227	F2	2	Di Renzo et al. (2004)
4	MSD	Rev81 × B73	157	F2:3	5	Dintinger et al. (2005)
5	MSD	MP705 × B73	174	RIL	4	Dintinger et al. (2014)
6	SCMV	FAP1360A × F7	121	F2	2	Duβle et al. (2000)
7	BYDV	I1 × I2	443	F2	7	Horn et al. (2015)
8	FoMV	B73 × Mo17	94	RIL	8	Ji et al. (2010)
9	MCDV	Oh1V1 × Va35	314	F2	2	Jones et al. (2004)
10	MSV	HI34 × Tzi4	87	RIL	1	Kyetere et al. (1999)
11	MSV	MAL13 × MAL9	100	S4	3	Lagat et al. (2008)
12	SCMV	Huangzao4 × Mo17	239	RIL	1	Liu et al. (2009)
13	MRDV	90110 × Ye478	120	RIL	7	Luan et al. (2012)
14	MMV	Hi31 × Kil4	96	RIL	1	Ming et al. (1997)
15	MSV	D211 × B73	165	F2:3	43	Pernet et al. (1999a)
16	MSV	CIRAD390 × B73	200	F2:3	7	Pernet et al. (1999b)
17	MRDV	Xi178 × B73	89	F8RIL	2	Shi et al. (2012)
18	MRDV	(NT401 × NT399) x NT409	211	BC1F2	2	Tao et al. (2013)
19	MSV	CML202 × Lo951	196	F2	2	Welz et al. (1998)
20	SCMV	D32 × D145	219	F2	5	Xia et al. (1999)
21	MFSV, MMV, MCMV, MDMV, SCMV	Oh1V1 × Oh28	260	RIL	12	Zambrano et al. (2014a)
22	MRFV	Oh1VI × Oh28	256	RIL	2	Zambrano et al. (2014b)
23	MRFV	Ki11 × B73	193	RIL	2	Zambrano et al. (2014b)
24	SCMV	Huangzao4 × Ye107	184	F2	16	Zhang et al. (2003)
25	MSV	TZIL07A01005 × TZIL07A01322	250	F2:3	4	Ladejobi et al. (2018)
26	MSV	Ku-R × GS-T	105	F2:3	10	Garcia-Oliveira et al. (2020)
27	MRDV	Zheng58 × D863F	241	RIL	4	Wang et al. (2019)
28	MRDV	Zheng58 × ZS301	247	RIL	1	Wang et al. (2019)
29	SCMV	TR56 × TR42	120	F3	1	Soldanova et al. (2012)
30	MLN	CKDHL120918 × CML494	306	F3	1	Awata et al. (2019)
31	MLN	CML543 × CML494	306	F3	4	Awata et al. (2019)
32	MLN	CKDHL120918 × CML543	306	F3	2	Awata et al. (2019)
33	MLN	CKLTI0227 × CKDHL120918	306	F3	6	Awata et al. (2019)
34	MLN	CKDHL0089 × CKDHL120918	306	F3	2	Awata et al. (2019)
35	MLN	CKDHL0221 × CKDHL120312	306	F3	3	Awata et al. (2019)
36	MLN	CKDHL0089 × CML494	306	F3	3	Awata et al. (2019)
37	SCMV	FAP1360A × F7	121	F3	3	Yuan et al. (2003)
38	MCMV, MLN	CML550 × CML504	219	DH	6	Sitonik et al. (2019)
39	MCMV, MLN	CML550 × CML511	111	DH	51	Sitonik et al. (2019)
	Total initial QTLs				196

Note: Maize streak virus (MSV), Mal de río cuarto virus (MRCV), Sugarcane mosaic virus (SCMV), Maize stripe virus disease (MSD), Barley yellow dwarf virus (BYDV), Foxtail mosaic virus (FoMV), Maize chlorotic dwarf virus (MCDV), Maize rough dwarf virus (MRDV), Maize mosaic virus (MMV), Maize dwarf mosaic virus (MDMV), Maize rayado fino virus (MRFV), Maize lethal necrosis (MLN), Maize chlorotic mottle virus (MCMV), Maize fine streak virus (MFSV).

The data of total 196 QTLs was summarized for MQTL analysis as mentioned in Table S1. QTLs with over 10 % phenotypic variance or those in 75th percentile of their respective studies [46] were included as the major effect QTLs. The mapping populations used in the MQTL study included F₂, F_3, F_2:3, F_2:6, F₅, BC (Backcross population), RILs (Recombinant inbred#lines), and DH (doubled haploid population) (Table 2). QTL and genetic map input files were converted to XML files using MetaQTL software (http://bioinformatics.org/mQTL), which were subsequently used as input files in BioMercator V4.2.3 to identify MQTL regions.

Table 2.

Meta-QTLs associated with resistance against 11 viral diseases and related information genes.

MetaQTL	Position (cM)	QTLs involved	Flanking marker	Range (bp)	CI (95 %)	Average phenotypic variance	Disease(s)	QTL No.	Population(s) No.	Candidate genes No.
MQTL1_1	114.3	exp4_qmrcv1 exp24_qscmv1	PZE.101129122- PZE.101140149	164,655,682–180,839,220	6.58	23.10	MRCV, SCMV	2	2	214
MQTL1_2	141.55	exp14_qmsv1.1	PZE.101157898-PZE.101158162	200,109,711–200,434,717	0.52	93.00	MSV	1	1	7
MQTL1_3	143.91	exp14_qmsv1.2 exp14_qmsv1.3	SYN19601-SYN26223	201,771,782–203,755,856	0.08	75.10	MSV	2	1	50
MQTL1_4	156	exp19_qmsv1.05.1 exp19_qmsv1.05.2 exp20_qmsv1	PZE.101169429- PZE-101171667	212,998,702–215,254,512	4.98	50.96	MSV	3	2	45
MQTL1_5	232.3	exp1_qmrcv1	PZE.101233035-PZD00069.3	281,235,735-298,054,056	43	13.00	MRCV	1	1	368
MQTL1_6	296.81	exp11_qfomv1.1 exp11_qfomv1.2	IDP7202-TIDP3263	252,316,945–256,345,800	10.17	21.80	FomvV	2	1	83
MQTL3_1	9.46	exp11_qfomv3 exp19_qmsv035.3.1	IDP2429-IDP7347	1,851,542–2,719,691	14.77	15.25	FomvV, MSV	2	2	41
MQTL3_2	77.6	exp2_qscmv3.1 exp32_qmsv3.1 exp42_qmln3.1 exp42_qmln3.2 exp43_qmln3.1 exp43_qmln3.2	PZE.103054855- PZE.103071320	66,947,062 −117,095,619	1.14	34.40	SCMV, MSV, MLN	6	4	336
MQTL3_3	98.8	exp25_qmmv3 exp25_qmcmv3.1 exp25_qmcmv3.2	PZD00007.1- PZE.103104806	165,216,844 −166,194,240	2.71	15.66	MMV, MCMV	3	1	1
MQTL3_4	106.67	exp2_qscmv3.2 exp5_qmsd3 exp28_qscmv3.1 exp28_qscmv3.2 exp28_qscmv3.3 exp28_qscmv3.4 exp12_qmcdv3	PZE.103112740-SYN13862	172,489,869–173,419,826	1.37	27.09	SCMV, MSD, MCDV	7	4	22
MQTL3_5	154.88	exp32_qmsv3.2 exp41_qmln3.4	umc60-gpm298	181,018,371–182,769,583	9.65	27.21	MSV, MLN	2	2	51
MQTL10_1	38.82	exp19_qmsv10.06.1 exp19_qmsv10.06.2 exp19_qmsv10.06.4	PZE-110014871-PZE-110034428	14,449,665–65,486,715	7	28.43	MSV	3	1	439
MQTL10_2	65.66	exp10_qbydv10.1 exp10_qbydv10.2 exp10_qbydv10.4 exp10_qbydv10.5 exp24_qscmv10	SYN4828-PZE-110082557	134,671,814–135,701,832	2.78	31.60	BYDV, SCMV	5	2	33
MQTL10_3	90.25	exp19_qmsv07.10.1 exp19_qmsv07.10.2 exp19_qmsv14.10.1 exp19_qmsv14.10.2 exp19_qmsv21.10.1 exp19_qmsv21.10.2 exp19_qmsv35.10.1 exp19_qmsv35.10.2 exp19_qmsv28.10.1 exp19_qmsv28.10.2 exp19_qmsv42.10 exp20_qmsv10 exp27_qmrfv10.1 exp27_qmrvf10.2	SYN21826-SYN6072	142,247,936–142,927,784	0	28.32	MSV, MRFV	14	3	25
Total								53		1715

2.2. Consensus map and QTL projection

The ‘ISU Integrated IBM 2009' map from Maize GDB (https://www.maizegdb.org/data center/map) containing 9073 distinct markers (RFLPs, SSRs and SNPs) spanning chromosome length of 2400.97 cM was utilized as a reference to create a consensus map. The published SNP map [47] was also integrated with the reference map to incorporate the QTL mapping investigations having SNP marker-based information. The CI of QTLs and position were taken from respective studies and for QTL studies where the position was not mentioned, it was assigned based on the flanking marker in the genetic map of a particular study. The major QTLs with refined CI and locations were projected on the consensus map employing BioMercator V4.2.3 [48].

2.3. Meta-analysis of QTLs

MQTL analysis was carried out using QTLs associated with traits imparting VDR. The QTLs for various traits like AUDPC; AUT; ANMT; APIT for MSV [11], symptoms like RE, IR and, VE for BYDV disease [12]; DSI at seedling, elongation, anthesis and grain filling for SCMV [49]; diseases severity, recovery and AUPDC for MSV [14]; severity and AUPDC for MLN and MCMV [13] and based on only screening against viral inoculations have been considered for viral diseases (Table S1). Following the generation of an integrated consensus map and QTL projection, the MQTL analysis was performed using BioMercator V4.2.3. The MQTL regions and QTL combinations were assessed using the models, such as AWE (Average weight of evidence), BIC (Bayesian information criteria), AIC (Akaike information content), AICc (AIC correction) and AIC3 (AIC 3 candidate models). Further, the one with the maximum likelihood was chosen. The models having the lowest AIC values in at least three among five models were used to identify the exact number of MQTLs per chromosome indicating the least information loss [39,50,51]. The position and 95 % CI (confidence interval) of the MQTLs were also obtained and flanking markers for MQTLs were discovered using MaizeGDB.

2.4. Validation of MQTLs with GWAS

The data of MTAs from existing GWA studies in maize for VDR was compiled (Table S3). The information about the physical position of these MTAs for VDR was compared to the interval identified MQTLs in this study. GWAS-MTAs flanked around 5 Mb region of these MQTLs considered as part of the MQTL region and included in this analysis [52]. The Circos plot was prepared using the RCircos package [53].

2.5. Candidate gene identification and gene network analysis

Physical position of each linked/flanking marker of each MQTL was determined using MaizeGDB (http://maizegdb.org/) and is based on B73 RefGen_v3. Using the ‘qTeller’ tool on MaizeGDB, the physical length of each MQTL was used to mine potential genes for viral disease resistance. Further, the gene annotation and ontology analysis-on candidate genes underlying MQTLs was done within-house Perl Script. The extracted files from the analysis provided information on gene transcripts number, their description and function, PFAM (protein family database) IDs and domain description, superfamily IDs, IPR (InterPro) IDs, GO (gene ontology) IDs and their descriptions along with their role in cellular, biological, cellular and molecular processes. The Plant Reactome Database was employed to understand the pathways of putative candidate genes [54]. The gene expression data for candidate genes at various growth and developmental stages of maize (B73 reference genome) was extracted from MaizeGDB [55]. The expression data was visualized by Morpheus software (https://software.broadinstitute.org/morpheus).

3. Results

3.1. Distribution of QTLs on maize genome for various viral diseases

A total of 196 initial QTLs compiled from 39 experimental studies for resistance against the 14 different viral diseases in maize were used for MQTL analysis. A total of 53 (27.04 %) out of 196 QTLs could be projected on the three chromosomes (Ch1, Ch3 and Ch10) across the maize genome (Table 1, Table 2). The 14 MQTLs on the three chromosomes explained phenotypic variance in the range of 13–93 %. Of the total 14 MQTLs, 6, 5 and 3 MQTLs were detected on Ch1, Ch3 and Ch10, respectively (Fig. 2). These MQTLs associated with the genomic regions controlling resistance against 11 viral diseases (SCMV, MRCV, MSV, FoMV, MLN, MMV, MCMV, MSD, MCDV, BYDV and MRFV). The analysis revealed that all three chromosomes had MQTL associated with resistance to both SCMV (four MQTLs 1_1; 3_2; 3_4; and 10_2) and MSV (eight MQTLs 1_2; 1_3; 1_4; 3_1; 3_2; 3_5; 10_5; and 10_3). Of the four MQTLs, three were located on Ch1 (1_2; 1_3; 1_4) and one on Ch10, i.e., MQTL10_1, which potentially conferred resistance against MSV and FoMV, respectively. Out of 53 initial QTLs, a maximum of 14 QTLs were present in MQTL10_3, while MQTL1_2 and 1_5 had the least number i.e. one each. The chromosome-wise distribution of these 53 initial QTLs was like 22 initial QTLs were present on Ch10 followed by 20 QTLs on Ch3 and 11 QTLs on Ch1. Although maximum initial QTLs were located on Ch10, MQTLs on Ch3 comprised initial QTLs responsible for resistance against eight viral diseases (FoMV, SCMV, MSV, MLN, MMV, MCMV, MSD and MCDV) followed by four diseases each on the Ch1 (MRCV, SCMV, MSV and FoMV) and Ch10 (MSV, BYDV, SCMV, MRFV) (Table 2, Fig S1). The two MQTLs were associated with a maximum of three diseases, viz., 3_2 (MSV, SCMV, MLN); 3_4 (SCMV, MSD, MCDV) followed by six MQTLs associated with two diseases, viz., 1_1 (SCMV, MRCV); 3_1 (FoMV, MSV); 3_3 (MMV, MCMV), 3_5 (MSV, MLN); 10_2 (BYDV, SCMV) and 10_3 (MSV, MRFV) and rest six MQTLs were associated with the single disease as listed in Table 2.

Fig. 2.

Distribution of MQTL across chromosomes Ch1, Ch3, and Ch10 of maize genome shows region associated with resistance to 11 viral diseases. Colored areas indicate the MQTLs regions with reduced confidence intervals and the chromosome number is displayed near the bar.

The MQTLs, 3_2 and 3_4 comprised of initial QTLs on four populations followed by MQTL10_3 from three populations; five MQTLs 1_1; 1_4; 3_1; 3_5; and 10_2 from two populations and the rest six MQTLs were from a single population. The value of the average CI of MQTLs (7.48) and initial QTLs (15.23) for VDR revealed that the CI of MQTLs was significantly reduced which provides an opportunity to maize breeders for their effective utilization in the VDR breeding programs.

3.2. Comparison of GWAS-based MTAs with MQTLs

The physical positions of MQTLs intervals were compared to MTAs positions extracted from existing GWA studies for maize viral diseases (Table S3). Ten MTAs from GWAS on VDR were chosen for their co-localization with 14 MQTLs (Fig. 3). Consequently, only 14.28 % (2 out of 14) MQTLs were verified with GWAS-MTAs. Notably, the 10 MTAs associated with specific were congruent with the QTLs for similar diseases within that MQTL region. The maximum seven MTAs were located in MQTL 10_2, followed by three MTAs in MQTL3_2. It was observed that, overall, the number of MTAs within MQTL intervals poorly corresponded with the number of diseases contained in that specific MQTL. Similarly, the number of candidate genes, initial number of QTLs and the number of co-located MTAs showed poor correlation. These verified MQTLs should be mined for candidate genes imparting VDR. The remaining MQTLs were not verified due to heterogeneity or diversity. The lack of verification arises from the wide genetic variability across studies and limited research on diseases linked to those MQTLs.

Fig. 3.

Circos plot illustrates the distribution of MQTLs and MTAs for VDR based on existing GWAS studies of maize viral diseases. The histogram (innermost track) displays the initial number of QTLs within MQTLs. The heatmap (next track) displays the number of candidate genes identified within MQTL interval (Red representing the maximum, blue intermediate and light blue the minimum value). The next track displays the associated disease and the chromosomal positions of MQTLs. The outermost tracks indicate the chromosomal positions of the MTAs for specific diseases that overlap with the corresponding MQTL interval, verifying the genomic regions responsible for disease resistance.

3.3. Candidate gene mining and understanding the function of constitutively expressed genes within MQTLs

This study identified 1715 candidate genes for VDR. Of these, 439 candidate genes were mined in MQTL10_1368 in MQTL1_5, 366 in MQTL3_2 and 214 in MQTL1_1, while only a single candidate gene was present in MQTL3_3 (Table 2 and Table S2). Mined candidate genes were evaluated for the gene expression at several growth and developmental stages of maize plant such as embryo, endosperm, whole seed, crown root, immature tassel and cob, V3, V5, V7, V9 and VT. These stages have been taken because the seed transmission of maize viruses results in significant yield loss [56,57]. The gene expression values of ≥1.0 were arranged to represent maximal expression. The constitutive defenses represent the permanent resource in plant repertoire to thwart away biotic stresses. Our focus on finding genes with constitutive expression patterns in almost all developmental stages of maize plants led to the identification of 279 constitutively expressed genes. The metabolic pathways for 22 LRR and MYB genes were observed using expression data of these constitutively expressed genes employing the Plant Reactome Database [54]. These constitutive genes govern various pathways such as amino acid biosynthesis and degradation, secondary metabolism, biochemical interactions of carbohydrate-containing compounds, nucleotide metabolism, and metabolic processes (Table 3). Furthermore, LRR and MYB proteins play a key role in signaling cascades, leading to plant antiviral defense responses [[58], [59], [60]]. These both have been identified as putative resistance genes against BYDV in wheat and barley [61,62]. A recent model implicates the role of MYB proteins as TFs, which initiate a cascade of signaling processes in plant cells to modulate very long fatty acid chains that eventually trigger hypersensitive response (HR). The interaction between MYB TFs and the promoter elements of defense-related genes is crucial for coordinating this defensive response [63]. The LRR proteins, known as intracellular resistance proteins play a crucial role in recognizing viral effector molecules and activating defense mechanisms [64] and are also reported as the dominant resistance gene against plant viruses [58]. Therefore, the gene expression profiles of LRR and MYB proteins within the MQTLs were examined. Eight genes, viz., Zm00001d030952, Zm00001d034033, Zm00001d034160, Zm00001d023825, Zm00001d025982, Zm00001d40831, Zm00001d042830 and Zm00001d042833 exhibited relatively higher expression levels across all stages of plant developmental (Fig. 4A and B). These genes are found to be involved in transcriptional, chromatin-remodelling, and ubiquitinoylation activities.

Table 3.

Pathways associated with the 22 constitutively expressed genes found in Plant Reactome database.

S. No.	Name of Pathways	Reaction Ratio	Genes involved
1.	UDP-N-acetylgalactosamine biosynthesis	0.003	Zm00001d030967
2.	Methionine biosynthesis II	0.006	Zm00001d031019
3.	Ureide biosynthesis	0.006	Zm00001d031024
4.	S-methylmethionine cycle	0.003	Zm00001d031088
5.	Arginine degradation	0.005	Zm00001d031128
6.	Cysteine degradation	0.006	Zm00001d031136
7.	UDP-D-xylose biosynthesis	0.003	Zm00001d032152
8.	Cysteine biosynthesis I	0.003	Zm00001d033174
9.	Histidine biosynthesis I	0.01	Zm00001d033228
10.	Allantoin degradation	0.008	Zm00001d034023
11.	S-adenosyl-L-methionine cycle	0.004	Zm00001d034032
12.	Phenylpropanoid biosynthesis	0.014	Zm00001d034072
13.	Nucleotide metabolism	0.008	Zm00001d034137
14.	Cytosolic glycolysis	0.005	Zm00001d034256
15.	Flavin biosynthesis	0.008	Zm00001d034319
16.	Proline biosynthesis V (from arginine)	0.004	Zm00001d034320
17.	Tryptophan biosynthesis	0.008	Zm00001d034343
18.	Galactose degradation II	0.006	Zm00001d034399
19.	Ascorbate biosynthesis	0.01	Zm00001d034452
20.	UDP-L-arabinose biosynthesis and transport	0.01	Zm00001d034460
21.	UDP-L-arabinose biosynthesis and transport	0.01	Zm00001d034493
22.	UDP-L-arabinose biosynthesis and transport	0.01	Zm00001d039361

Note: Reactions Ratio means ‘The total reactions in the pathway divided by the total number of reactions for the entire species for the selected molecular type.’

Fig. 4.

Relative expression of genes containing LRR/MYB domain.

(A) Gene expression at various plant growth and development stages. These included 5 stages of embryo (16, 18, 20, 22 and 24 days after pollination/DAP), 6 stages of endosperm (12, 16, 18, 20, 22 and 24 DAP), 11 stages of whole seed (2, 4, 6, 8, 10, 12, 18, 20, 22 and 24 DAP), 4 stages of crown root (Node 1_3, 4, 5 in V7 stage, Node 5 in V13 stage), immature tassel and cob (2 stages), 2 stages of V3 (shoot apical meristem/SAM & stem and topmost Leaf), 4 stages of V5 (first elongated internode, bottom of transition leaf, shoot tip and stage 2 leaf tip), 2 stages of V7 (bottom and tip of transition leaf), 5 stages of V9 (4th elongated internode, immature leaf, 8th leaf, 11th leaf, 13th leaf) and VT/Tassel emergence stage (1 stage; 13th leaf). (B) Radar-plot of constitutively expressed proteins containing LRR/MYB domain. The numbers around the circle represent the 42 developmental stages as mentioned in 4A. The number in between the circle represents the level of gene expression.

4. Discussion

The viral diseases reduce maize yields, putting food security and industry grain supply under threat [5,65,66]. A virus is obligate intracellular and hence needs a host cell for replication [67]. The most environmentally friendly, economical, and successful way to combat viral infections is to breed for genetic resistance [68], evidenced by its efficacy in resistant maize hybrids [66]. To determine genetic resistance three important points should be taken into consideration, i) inheritance of resistance, ii) number of genes involved, and iii) the mechanism of gene action [68]. The Resistance (R)-genes and their analogs (RGAs) are highly evolving and maize has much more diverse RGAs and mechanisms than wheat and rice due to its outcrossing nature [69,70]. Sufficient variation in the adapted germplasm allows maize breeders to combine major/minor-effect QTLs to achieve an effective quantitative resistance against viral diseases [70]. Hence, this study used the previously mapped QTLs (196) for VDR across a diverse range of populations and environments for the MQTL analysis in maize. Our study found significant MQTLs with a reduced interval (CI) across the maize genome and identified candidate genes for single or multiple VDR. The newly identified MQTL regions and underlying candidate genes could support maize breeders for varietal improvement and hybrid development against major viral diseases. Two out of 14 MQTLs agreed with GWAS-based MTAs, which is quite low but agrees with the findings of previous studies. The possible reason for the low correlation could be the difference in the germplasm material across QTL mapping and GWAS studies, as well as the fact that both approaches do not include the whole genetic variation of the gene pool [71,72]. Moreover, a relatively low number of GWAS studies were available for concerned viral diseases.

4.1. Candidate gene identification in MQTLs for VDR in maize

Among the 14 MQTLs identified on three chromosomes namely, Ch1, Ch3 and Ch10 for resistance against 11 maize viral diseases (SCMV, MRCV, MSV, FoMV, MLN, MMV, MCMV, MSD, MCDV, BYDV, MRFV). Our findings corroborate the previous observations made by Redinbaugh and Pratt [73] regarding the existence of clusters of loci conferring VDR in maize, specifically on Ch 1, 3, and 10. Our findings show good agreement with an earlier meta-QTL analysis [46] that identified Ch 1, 6, and 10 as hot-spots for major-effect QTL for VDR. This indicates that these genomic regions are key breeding targets for improving resistance against viral diseases. Additionally, it has been observed that QTLs for viral resistance tend to be clustered together, suggesting the involvement of a limited number of loci [44]. For example, the msv1 (Maize Streak virus resistance1) locus has been reported in bin 1.05 [73]. Similarly, a major QTL for msv1 in the same bin on Ch 1 (1.04/1.05) has been identified, consistent with earlier studies [31]. In our research, the QTLs for msv1 were also discovered in MQTL1_2, MQTL1_3, and MQTL1_4, confirming the existence of genomic regions on Ch 1 linked with resistance to MSV (Maize Streak Virus). A tyrosine kinase family protein (GRMZM2G046848/Zm00001d029829) was also reported as a candidate gene for MSV on Ch1 [31]. We also identified additional regions associated with MSV resistance, including MQTL3_1, MQTL3_2, MQTL3_5 on Ch3 and MQTL10_1, MQTL10_3 on Ch10. The occurrence of the MQTLs on the three chromosomes is consistent with earlier reports on QTLs conferring resistance against MSV [31,63]. Several major and minor genes with additive action [74,75] are reported in maize that impart complete or partial resistance against MSV. Among them, genes encoding ubiquitin conjugate protein, bowman-birk type trypsin inhibitor, mitogen-activated protein kinase (MAPK), and pathogenesis-related protein (Table S4) are associated with QTLs identified for wheat streak mosaic virus resistance in rice, wheat and brachypodium [76]. The ZmMPK5 is involved in plant defense-signaling pathways of various biotic and abiotic stresses [38,77].

The MQTL1_1, MQTL3_2, MQTL3_4, and MQTL10_2 for SCMV resistance identified in our analysis align with previous studies [30,49,66]. The SCMV2/ZmABP1 gene, which encodes an auxin-binding protein, has been reported on chromosome 3 [78]. QTL qscm2 and qscm, reported on Ch 3 and 10, respectively, in previous studies, align with our findings in MQTL3_2 and MQTL10_2. Notably, our identification of the SCMV resistance region on chromosome 1 at MQTL1_1 is a novel discovery, suggesting a potential consensus region diverse germplasm for SCMV resistance. However the resistant QTLs showed stage specificity as QTLs for SCMV resistance on Ch3 and Ch10 were consistently detected at all four stages of maize plant i.e. seedling, elongation, anthesis and grain-filling stage, whereas the resistance QTLs on Ch6 were detected at elongation and anthesis [49]. In our study, the presence of candidate genes that have been previously identified for SCMV/SCMV1 resistance locus [29,30,66,[79], [80], [81], [82]] was confirmed in all four MQTL regions. Similarly, auxin response factors, Rho GTPase gene, and syntaxin proteins previously identified for the SCMV2 locus [29,30,78,81,83,84] remain within four MQTL regions (Table S4). Furthermore, various researchers [29,30,66,81] reported the potential candidate genes associated with the Scmv1 and Scmv2 loci (Table S4), also observed in the present study in respective MQTL regions. The role of the GTPase gene (GTP-Binding proteins) against biotic stresses has also been reported [37]. The MQTL10_2 was also linked with BYDV resistance which contained the initial QTLs from Horn and coworkers [12]. Evidence supports the role of eukaryotic translation initiation factors in virus resistance in maize and various plants [16,85,86]. However, a study examining the correlation between various translation initiation factors and BYVD resistance in maize found no association between the two [87]. Interestingly, our investigation identified the candidate gene Zm00001d025979 for initiation factor in MQTL10_2, which also harbors QTLs for BYDV resistance from a related study [88]. Our results reinforce the role of initiation factors in BYDV resistance in maize.

Two MQTLs have their roles in resistance against three diseases, viz., MQTL3_2 for MSV, SCMV, MLN and MQTL3_4 for SCMV, MSD, and MCDV. The candidate genes in the MQTL3_2 region are associated with MLN resistance [89]. Furthermore, common candidate genes for other viral diseases [76,90,91] in MQTL3_2 such as MAP-kinase eight LRR-RLK genes in this region indicate its significance in imparting multiple VDR (Table S4). Furthermore, the MLN resistance of MQTL3_2 verified by GWAS-MTAs supports its function in MLN resistance in maize.

MQTL3_4 region had candidate genes including MADS-box TF family protein, NAC domain-containing protein, Nucleotide-diphospho-sugar transferases which play important roles in resistance against viral and fungal diseases. The NAC-TFs develop resistance in plants through effector-triggered immunity and hypersensitive response [92]. The SINAC61 TF demonstrated to be effective against the tomato leaf curl virus using virus-induced gene silencing [93]. The NAC035 and NAC072 are also responsible for drought tolerance in rapeseed, demonstrating their wider importance in abiotic and biotic tolerance development [40,41].

In addition to QTLs of SCMV, the MQTL1_1 also contains QTLs for MRCV. Another region MQTL1_5 also showed an association with MRCV. One group of researchers [94] screened the maize germplasm from Argentina for incidence and severity of MRCV and identified candidate genes adjacent to the serine-threonine (Ser-Thr) protein kinase encoding gene (involved in plant defense response) on Ch1 [95]. The existence of candidate genes in these regions (Table S4) validates their association with MRCV resistance [94].

In addition to MSV, the MQTL3_1 confers FoMV resistance with MQTL1_6 exclusively linked with this disease. Both regions have initial QTLs for FoMV from a previous study [27] with the phenotypic variance from 18.1 to 22.3 %. The presence of MQTLs on Ch1 and Ch3 for FoMV is consistent with previous reports [27,73]. Among these genes, elongation factor eEF1A interacts with viral replicases in many plant viruses, helping in their replication and movement [[96], [97], [98]]. Silencing and mutation of the genes encoding these factors could impart resistance to viruses [98,99]. To the best of our knowledge, the current study is the first to point (hint) out the putative candidate genes for FoMV resistance across diverse germplasm and environments, the functional roles remain to be established though.

The MQTL3_5 also showed the presence of initial QTLs for MLN resistance with the different populations [20] along with MSV resistance. The candidate genes identified in this region (Table S4) remain crucial for development of resistance against various plant viruses [91,94]. Research has demonstrated the role of AtMYB96, a molecular linker for crosstalk between salicylic acid and abscisic acid (ABA)-signaling pathways, in conferring resistance against pathogens of Arabidopsis [100]. The ABRE (ABA-responsive element), a cis-regulatory element has a functional role in the promoter region of stress-responsive genes [36]. Interestingly, MQTL3_3 is linked with two diseases such as MMV and MCMV, but it has only one candidate gene Zm00001d042409 for the hypothetical protein, which awaits functional validation.

The MQTL10_2 had initial QTLs for BYDV and SCMV resistance and harbors 33 candidate genes. The MTAs from GWA studies strengthen the role of MQTL10_2 in BYDV resistance in maize. The MQTL10_3 also contains various earlier reported candidate genes like RING/FYVE/PHD zinc finger superfamily protein, SAUR-like auxin-responsive protein, ethylene-responsive TF (Zm00001d026271), protein kinase superfamily for other viral diseases in maize which could be considered as candidate genes for MRFV resistance. Hence, this is the first report to suggest putative candidate genes for FoMV, MMV and MRFV, although that needs to be validated at the functional level.

4.2. Analysis of the constitutively expressed genes in the identified MQTLs

Constitutive plant defenses, which encode various physio-chemical barriers form an important strategy against biotic stresses. These represent the genetically encoded differences between susceptible and resistant varieties. Constitutive plant defense genes are expected to confer broad-spectrum resistance [101] and hence are potential targets for breeding viral resistance. The constitutive genes within MQTL regions for VDR are involved in five pathways: amino acid biosynthesis and degradation, secondary metabolism, biochemical interactions of carbohydrate-containing compounds, nucleotide metabolism and metabolic processes.

Amino acids and their derivatives are pivotal to plant defense. The present study suggests a role of methionine, histidine, proline and tryptophan biosynthesis pathways, with arginine degradation and cysteine being involved in both biosynthesis and degradation [102]. Plant methionine cycle enzymes are employed by viruses to cause infection [103]. These enzymes, together with viral proteins, act in the form of temporary structural-functional complexes and regulate viral infectivity and host response. Arginine, along with other amino acids, is found in peptides that possess antiviral activity [104]. Arginine can potentially inactivate enveloped viruses by interacting with and destabilizing either protein or lipids or by creating holes in the viral envelope. S-methylmethionine is a non-protein amino acid, that maintains the photosynthetic activity during infection by MDMV [105]. The importance of ferroptosis, a programmed cell death (PCD) dependent on iron in plant cells has also been reviewed [106]. Cysteine deficiency in turn causes depletion of glutathione and cell death by reactive oxygen species (ROS). A similar strategy might be in play in the case of plant virus-induced changes in the host metabolism. Given that both cysteine degradation and biosynthesis pathways are involved in the MQTLs related to VDR, the balance between the two metabolic pathways appears to be a key regulatory point deciding the cell fate upon viral infection.

Among the secondary metabolites, phenylpropanoids respond to host cell defense by acting as antioxidants [107]. Similarly, flavonoids are involved in antiviral defense [108]. Researchers have observed the antiviral effects of synthetic ureides containing urea [109]. Allantoin, a by-product of purine breakdown accumulates in stressed plants and triggers the ABA and stimulates stress-related gene expression [110]. Abiotic and biotic stress-related genes are known to show cross-talk amongst each other [111]. Ascorbic acid is involved in defending Brassica rapa cultivars against Turnip Mosaic virus [112].

Nucleotide sugars are vital for cellular metabolism, playing a key role in protein and lipid glycosylation [113]. Plant lectins, including N-acetylgalactosamine specific plant lectins, have antiviral activity against SARS-CoV (Severe Acute Respiratory Syndrome Corona virus) and FIPV (Feline Infectious Peritonitis Virus) in vitro [114]. The significance of plant cell wall in antiviral response has been well demonstrated. UDP-xylose is a crucial component for the synthesis of xylan and xyloglucan, which affect the cell wall integrity [115,116]. Glycosylation plays a role in regulating phenylpropanoid functions in plants [117]. UDP-arabinopyranose is a significant constituent of the plant cell wall and other secondary metabolites [118]. The above pathways and the genes associated with them constitute an important resource for allele mining among the susceptible and resistant genotypes to map VDR and elucidate the resistance mechanisms.

4.3. Comparison between metabolic pathways for VDR and fungal disease resistance (FDR)

The metabolic processes involved in VDR are different from those involved in FDR, a proposition we made in our earlier study [119]. The main difference is with respect to secondary metabolism. While the FDR pathways included kievitone biosynthesis, methylerythritol pathway, the VDR includes a myriad of pathways viz. phenylpropanoids, ureides, allantoins, lectins, and especially N-acetylgalactosamine. Amino acid metabolism contributes to resistance against both fungal and viral agents. In the case of FDR, growth and development pathways are involved, which may enhance the transition to the reproductive stage. In VDR, metabolic processes that facilitate secondary metabolism (for example, glycosylation regulating the phenylpropanoid activity) were observed in the conserved gene network linked to VDR. This implies that the ideal genotype for resistance against fungal and viral agents may differ from each other. On the other hand, the metabolic processes that are common to both the resistances, especially amino acid metabolism, are relatively important for imparting simultaneous resistance against the fungal and viral agents. The information generated in the present study can be utilized to undertake allele mining to extract favourable alleles linked to VDR in maize. Understanding the antiviral mechanisms will in turn reveal the molecular targets for precise genetic changes in the maize germplasm via modern gene editing tools.

5. Conclusions

The alarming population growth and changing climate-induced biotic and abiotic stresses, especially in the last few decades imposed the biggest challenge to safeguard global food security. Viral diseases cause substantial economic losses in maize, demanding the development of high-yielding VDR maize cultivars. An in-depth understanding of VDR-associated genomic regions and the functional roles of candidate genes or expressed proteins in the regulation of disease incidence and severity is of paramount significance. Our study provides an example of leveraging the existing data and information on QTL mapping for VDR, to pinpoint crucial genomic regions for VDR with much narrow CIs. Most of the genomic regions harbor genes for multiple viral diseases, providing clues to develop multiple VDR cultivars in maize. The confirmation of identified candidate genes with previous studies underscores their potential for breeding VDR maize. Furthermore, the confirmation of MQTLs for particular diseases with MTAs in various GWA studies for VDR further strengthens the confidence in the deployment of these QTLs in breeding programs. Even though specific MQTLs were linked to a single disease, these also included candidate genes for multiple VDR. MQTLs shared many key candidate genes, viz., TFs (MYB, NAC, MADS-box, bHLH, EFs, IFs, kinases (LRR, Ser/Thr), syntaxin protein and other important enzymes encoding genes such as Rho GTPase, oxidoreductase, cycloartenol synthase, etc. This research also revealed the probable candidate genes for FoMV, MMV and MRFV which is a new report as per our best knowledge. The use of “MAS-friendly MQTLs” imparting resistance against specific or multiple viral diseases should be given priority in maize breeding programmes. Moreover, the identified candidate genes and gene networks can be used to understand better the molecular mechanism of defense responses and pathways related to resistance against multiple stresses. These common genomic regions can be used to develop more efficient resistant cultivars. The study also paves the way for functional and synteny analyses for a better understanding of host plant resistance against viral diseases.

CRediT authorship contribution statement

Mamta Gupta: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mukesh Choudhary: Writing – review & editing, Visualization, Methodology, Formal analysis. Alla Singh: Writing – original draft, Visualization, Validation. Seema Sheoran: Writing – original draft, Methodology. Harish Kumar: Writing – original draft, Software, Investigation, Formal analysis, Data curation. Deepak Singla: Validation. Abhishek Bohra: Writing – review & editing. Sujay Rakshit: Writing – review & editing, Supervision, Conceptualization.

Data availability

The supporting data of the findings in this study is provided in the manuscript and supplementary information. Raw data generated to conduct and support the findings of this study can be made available from the corresponding author, upon reasonable request.

Funding

The fund was not received for this study.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Abhishek Bohra is an associate editor of Heliyon Agriculture. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is/are the supplementary data to this article.

Multimedia Component 5

The bar diagram representing the number of MQTL regions associated with different viral diseases on chromosomes 1, 3, and 10 on the maize genome. The colors depicting diseases as mentioned in the diagram.