Next Article in Journal
Transcriptome and Metabolome Reveal Sugar and Organic Acid Accumulation in Rosa roxburghii Fruit
Next Article in Special Issue
Genetic Diversity and Population Structure Analyses in Bitter Gourd (Momordica charantia L.) Based on Agro-Morphological and Microsatellite Markers
Previous Article in Journal
Savinin Triggers Programmed Cell Death of Ray Parenchyma Cells in Heartwood Formation of Taiwania cryptomerioides Hayata
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 12
Issue 17
10.3390/plants12173035
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Comprehensive Genome-Wide Investigation of the Cytochrome 71 (OsCYP71) Gene Family: Revealing the Impact of Promoter and Gene Variants (Ser33Leu) of OsCYP71P6 on Yield-Related Traits in Indica Rice (Oryza sativa L.)

by
Bijayalaxmi Sahoo
1,2,
Itishree Nayak
1,3,
C. Parameswaran
1,*,
Mahipal Singh Kesawat
4,*,
Khirod Kumar Sahoo
2,
H. N. Subudhi
1,
Cayalvizhi Balasubramaniasai
1,
S. R. Prabhukarthikeyan
5,
Jawahar Lal Katara
1,
Sushanta Kumar Dash
1,
Sang-Min Chung
6,
Manzer H. Siddiqui
7,
Saud Alamri
7 and
Sanghamitra Samantaray
1
1
Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack 753006, India
2
Department of Botany, Ravenshaw University, Cuttack 753006, India
3
Department of Botany, Utkal University, Bhubaneswar 751004, India
4
Department of Genetics and Plant Breeding, Faculty of Agriculture, Sri University, Cuttack 754006, India
5
Crop Protection Division, ICAR-National Rice Research Institute, Cuttack 753006, India
6
Department of Life Science, Dongguk University-Seoul, Ilsandong-gu, Goyang-si 10326, Gyeonggi-do, Republic of Korea
7
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Plants 2023, 12(17), 3035; https://doi.org/10.3390/plants12173035
Submission received: 13 July 2023 / Revised: 17 August 2023 / Accepted: 21 August 2023 / Published: 23 August 2023
(This article belongs to the Special Issue Genetic Diversity, Germplasm Resources, and Biotechnological Applications for Sustained Crop Improvement)
Browse Figures
Review Reports Versions Notes

Abstract

:
The cytochrome P450 (CYP450) gene family plays a critical role in plant growth and developmental processes, nutrition, and detoxification of xenobiotics in plants. In the present research, a comprehensive set of 105 OsCYP71 family genes was pinpointed within the genome of indica rice. These genes were categorized into twelve distinct subfamilies, where members within the same subgroup exhibited comparable gene structures and conserved motifs. In addition, 105 OsCYP71 genes were distributed across 11 chromosomes, and 36 pairs of OsCYP71 involved in gene duplication events. Within the promoter region of OsCYP71, there exists an extensive array of cis-elements that are associated with light responsiveness, hormonal regulation, and stress-related signaling. Further, transcriptome profiling revealed that a majority of the genes exhibited responsiveness to hormones and were activated across diverse tissues and developmental stages in rice. The OsCYP71P6 gene is involved in insect resistance, senescence, and yield-related traits in rice. Hence, understanding the association between OsCYP71P6 genetic variants and yield-related traits in rice varieties could provide novel insights for rice improvement. Through the utilization of linear regression models, a total of eight promoters were identified, and a specific gene variant (Ser33Leu) within OsCYP71P6 was found to be linked to spikelet fertility. Additionally, different alleles of the OsCYP71P6 gene identified through in/dels polymorphism in 131 rice varieties were validated for their allelic effects on yield-related traits. Furthermore, the single-plant yield, spikelet number, panicle length, panicle weight, and unfilled grain per panicle for the OsCYP71P6-1 promoter insertion variant were found to contribute 20.19%, 13.65%, 5.637%, 8.79%, and 36.86% more than the deletion variant, respectively. These findings establish a robust groundwork for delving deeper into the functions of OsCYP71-family genes across a range of biological processes. Moreover, these findings provide evidence that allelic variation in the promoter and amino acid substitution of Ser33Leu in the OsCYP71P6 gene could potentially impact traits related to rice yield. Therefore, the identified promoter variants in the OsCYP71P6 gene could be harnessed to amplify rice yields.

1. Introduction

The cytochrome P450 (CYP450) gene family plays a critical role in plant growth and developmental processes, nutrition, and detoxification of xenobiotics in plants [1,2,3,4,5,6,7]. In addition, CYP450 has been implicated in diverse metabolic reactions and targets a wide range of biological molecules. These biosynthetic reactions lead to diverse plant hormones, fatty acid conjugates, lignin, secondary metabolites, and numerous defensive compounds [8,9]. For instance, CYP706B1 and CYP82D113 participate in the biosynthesis of gossypol, while CYP71AV1 is implicated in the biosynthesis of artemisinin in plants [1,10]. The CYP450 genes have been identified and characterized in various organisms such as animals, bacteria, humans, and plants [1,2,3,4,5,11,12,13,14,15]. The plant CYP450 gene family is categorized into two primary groups: the A-type and the non-A type [16,17]. Further, several research groups reclassified them into eleven classes based on the available numerous genome sequences. The A-type is grouped into the CYP71 class, while the non-A type is subdivided into ten classes (CYP51, CYP72, CYP74, CYP85, CYP86, CYP97, CYP710, CYP711, CYP727, and CYP746) following a unified nomenclature system [17,18,19,20]. The members of CYP73, CYP84, and CYP98 gene families play a role in the phenylpropanoid biosynthetic pathway, contributing to the synthesis of various phenolic compounds. These compounds include substances such as flavonoids, suberin, polyphenols, and lignin [1,2,3,4,5]. Further, plant CYP450 enzymes have been linked to a wide array of metabolic pathways, giving rise to both primary and secondary metabolites [20]. For example, the majority of genes associated with the biosynthesis pathways of flavonoids and isoflavonoids are members of the CYP450 gene family. Notable examples include CYP73, CYP75A, and CYP93C [21]. Remarkably, the diversity in the P450 gene family had a significant impact on the emergence of novel metabolic pathways during the evolution of land plants. A classic example is cytochrome P450 93C, which is identified in leguminous crops and enzymes belonging to the CYP93C subfamily and participates in the legume-specific isoflavonoid biosynthesis pathway [22,23,24,25,26].
In rice, the gene OsCYP71P6 (BGIOSGA018523) is a homolog of CYP71A1 (LOC_Os12g16720). This gene encodes a CYP450 monooxygenase in rice that exhibits tryptamine 5-hydroxylase activity. Its function involves catalyzing the conversion of tryptamine into serotonin [27,28]. Phytoserotonin (5-hydroxytrptamine) is found in different tissues of plants, is involved in growth and development, and regulates tolerance to different biotic and abiotic stresses [27]. Further, the synthesis of serotonin through the amino acid tryptophan is highly conserved and distinct within plants. Consequently, alterations in the serotonin biosynthesis pathway have a direct impact on various plant developmental processes. A loss-of-function mutation of OsCYP71P6, involved in the activity of the tryptamine-5-hydroxylase gene and serotonin synthesis, resulted in brown plant hopper (BPH) and stem borer resistance in rice by altering pest feeding behavior. However, yield traits were also affected by the loss-of-function mutation of OsCYP71P6 in rice [28]. Rice holds a prominent position as a staple food in countries across South Asia [29,30]. The popularity of rice varieties among farmers is greatly determined by their panicle architecture, including primary and secondary branches, the count of spikelets per panicle, stable yield, and milling properties [31]. In rice, several genes have been identified and characterized, such as Gn1, DEP1, GS3, SPL18, and IPA1, which regulate panicle architecture, grain number, and yield [32]. Additionally, many of the genes associated with yield also play a role in regulating other crucial traits in rice. For example, elevated expression of the IPA1 gene, which encodes OsSPL14 in young panicles, correlates with an increased number of primary branches [33] and blast disease resistance [34]. Similarly, the Dense and Erect Panicle 1 (DEP1) gene regulates nitrogen use efficiency and yield [35]. Although the OsCYP71P6 gene provides tolerance to insect pests, reduced serotonin differentially regulates senescence and negatively affects rice yield [28]. Gene sequence polymorphisms are common occurrences in both coding and promoter regions, leading to variations in gene transcription. These variations play a role in phenotypic adaptation among diverse rice varieties [36,37]. The availability of the rice 3K panel significantly aids in detecting single nucleotide polymorphisms and insertion–deletion (in/dels) within chromosomal regions or genes [38]. Previously, an insertion present in the 3′UTR of the TPP7 (trehalose phosphate phosphatase 7) gene was found to be associated with enhanced anaerobic response in rice [39].
Following recent advancements in DNA-sequencing technology, there has been a surge in the number of sequenced plant genomes. This has facilitated genome-wide analysis and characterization of the CYPP450 gene family in various crop species, including Arabidopsis thaliana [17,40], Glycin max [7], Morus notabilis [10], Nicotiana tabacum [41], and Linum usitatissimum [42]. The CYP450 gene family is extensive, and only a limited number of its members have undergone comprehensive structural and functional characterization. Additionally, there remains a scarcity of information regarding the CYP71 gene family in rice. The evolutionary lineage, gene and motif arrangement, cis-regulatory element involvement, and transcription kinetics of the CYP71 gene family in rice are currently unexplored. A comprehensive genome-wide investigation and analysis spanning various plants from different evolutionary branches is imperative. Such information could potentially enrich our comprehension of the evolutionary lineage and biological roles of the CYP71 gene family in the plant kingdom. The complete genome sequence of indica rice (Oryza sativa Indica) is publicly accessible, enabling us to conduct a comprehensive genome-wide analysis of the CYP71 gene family in rice [43]. Rice holds significance as both a vital cereal crop and a model plant in scientific research [29,30,44]. Hence, in this work, we conducted a comprehensive genome-wide analysis of the CYP71 gene family in rice, employing a range of computational tools. Further, we investigated the physical and biochemical characteristics, gene structure, motif composition, chromosomal distribution, gene duplication events, 3D structural aspects, and expression patterns of members of the OsCYP71 gene family across different tissues and developmental stages. Our hypothesis centers on the notion that the insertion and deletion (in/dels) variations within the OsCYP71P6 gene in rice could provide insights into the gene’s impact on yield-related traits. Our objective is to pinpoint specific in/dels variations unique to OsCYP71P6 and validate these sequence polymorphisms to ascertain their effects on yield-related traits in rice. Furthermore, through linear regression models, we identified eight promoters and a gene variant (Ser33Leu) of OsCYP71P6 that exhibited associations with spikelet fertility. Additionally, we validated various alleles of OsCYP71P6, identified via in/dels polymorphism in 131 rice varieties, to determine their effects on yield-related traits. The identified promoter and gene variants within OsCYP71P6 hold potential for enhancing rice yield. Collectively, this study offers a valuable resource for further comprehending the specific functions of OsCYP71 gene family members in rice.

2. Results

2.1. Genome-Wide Identification and Phylogenetic Tree Analysis of OsCYP Genes in Rice

In this study, a total of 105 CYP71 genes were successfully identified in rice (Oryza sativa Indica). These 105 OsCYP71 genes were found to encode proteins ranging in length from 321 to 1165 amino acids long (Table 1 and Table S1).
The molecular weight of the encoded protein is between 36.35 kda (OsCYP71K2) and 130.4 kda (OsCYP71AB12). The isoelectric point sizes ranged from 5.45 (OsCYP71K2) to 9.90 (OsCYP71AB13); among them, 82 proteins encoding OsCYP71 were identified by PI > 7, suggesting that most OsCYP proteins have a high proportion of basic amino acid. We observed GRAVY values for 105 OsCYP71 proteins between −0.409 (OsCYP71E2) and 0.197 (OsCYP71U3). Further, subcellular localization projections indicated that 100 OsCYP71 proteins were localized in the endomembrane system. The rest are mapped to plasma membrane (OsCYP71C1, OsCYP71Z4, OsCYP71V1, OsCYP71E2, and OsCYP71C3) and mitochondrial membrane (OsCYP71P6). In addition, we also examined the molecular weight of OsCYP71 to the pI to determine the distribution of OsCYP71 proteins (Figure S1). The outcomes of this analysis unveiled a pattern where the majority of OsCYP71 proteins share a comparable molecular weight and isoelectric point. To evaluate the evolutionary connections among OsCYP71 genes, we employed CYP71 genes from Arabidopsis and tomato. Using MEGA 11 software, we constructed a phylogenetic tree that encompassed 105 CYP71 proteins from rice along with 42 CYP71 proteins from Arabidopsis and 43 CYP71 proteins from tomato (Figure 1, Table S2).
The results showed that 105 OsCYP71 genes could be divided into 12 subfamilies. Group VII had 74 members, whereas Groups I, II, III, IV, V, VI, VIII, IX, X, XI, and XII had 0, 8, 5, 0, 13, 2, 3, 0, 0, 0, and 0 members, respectively (Figure S2).

2.2. Chromosome Distribution and Gene Duplication Analysis of OsCYP71 Genes

To further investigate gene duplication events in rice, we performed a chromosomal mapping analysis of identified OsCYP71 family genes. A total of 105 OsCYP71 genes were mapped onto 11 chromosomes (Figure 2A). Chromosome 2 had the most OsCYP71 genes (20 genes) and chromosome 7 had the least OsCYP71 genes (1 gene). The 105 OsCYP71 genes were distributed unevenly on 12 chromosomes of rice (Figure 2B).
The four OsCYP71 genes were distributed across a genomic scaffold (Table 1). Gene duplication plays a crucial role in the evolution of gene families. In order to study the evolutionary relationship of OsCYP71 genes, the gene duplication events of OsCYP71 genes were analyzed in this study (Figure 3 and Figure S3).
A total of 36 duplicated gene pairs were detected (Table S3). Further, the Ka/Ks score for these genes was less than one, indicating that there had been a strong purifying selection with slight changes in subsequent gene duplication. These findings highlight the fact that the OsCYP71 family genes have undergone a conserved evolutionary process.

2.3. Gene Structure and Basic Motif Analysis of OsCYP71 Gene

Analyzing gene structure provides valuable insights into the conserved features and evolutionary distinctions of CYP proteins in rice. Through this analysis, we observed that the number of introns varied across different subfamilies (Figure 4).
The number of introns of all CYP subfamily genes ranged from zero to four, whereas most of the genes have between one and two introns. The maximum four introns were detected in OsCYP71E2, while OsCYP71AD, OsCYP71C3, OsCYP71C4, OsCYP71AB7, OsCYP71W5, OsCYP71U3, OsCYP71T4, OsCYP71T7, OsCYP71T5, OsCYP71T8, OsCYP71T6, and OsCYP71T9 were intronless (Figure 4). Further, to investigate the characteristics of OsCYP71 proteins, we identified ten conserved motifs in OsCYP71 proteins (Figure 5A,B).
In this study, it was observed that motifs 1, 2, 3, 6, 7, and 10 exhibited a high degree of conservation across the majority of OsCYP71 proteins. Additionally, through amino acid sequence alignment, it was noted that OsCYP71 proteins contain conserved SRS and heme-binding motifs (Figure S4A). We also conducted predictions for the 3D structure of OsCYP71 proteins (Figure S4B). Numerous conserved motifs identified within these OsCYP71 family proteins are likely involved in the regulation of diverse metabolic reactions.

2.4. Promoter Element and GO Enrichment Analysis

To gain insights into the potential functions of the OsCYP71 family genes, we employed the Plant-CARE online web server to analyze the 1500 bp upstream sequences of these genes. This analysis revealed the presence of numerous cis-regulatory elements, encompassing phytohormone responsiveness, light response, cell cycle regulation, circadian rhythm, seed-specific regulation, and responses to defense and stress (Figure 6A,B).
In terms of hormonal response, there are abscisic acid-responsive elements (ABRE), auxin-responsive elements (TGA-element, AuxRE, TGA-box), gibberellin-responsive elements (Tatc-box, GARE-motif, P-box, TATC-box), salicylic acid-responsive elements (TCA-element, SARE), methyl jasmonate-responsive elements (CGTCA-motif, TGACG-motif), and so on (Table S4). MeJARE, ABRE, and AuxRE were found in the promoter regions of most genes, with MeJARE and ABRE being found in almost all OsCYP71 genes. SARE- and GARE-motif elements were found in a few promoter regions of OsCYP71 genes. Stress response includes elements related to anaerobic induction (ARE), low temperature response (LTR), drought induction (MBS), defense and stress responses (TC-rich repeats), and low temperature and salt stress (DRE). Among them, the ARE element was found in the promoter regions of 40 genes, the LTR element was found in the promoter regions of 38 genes with 1–2 elements, and TC-rich repeat elements were found in 21 gene promoter regions with 1–2 elements. In addition, MBS elements, considered drought induction-related elements, were also present in the promoter regions of 43 genes (Table S4). These results suggest that the OsCYP71 family genes may play an important role in plant development and stress responses through the regulation of multiple cis-regulatory elements in rice.
In order to further understand the function of OsCYP71 family genes, we conducted a gene ontology (GO) enrichment analysis. Utilizing AgriGO, we effectively annotated and attributed GO terms to all OsCYP71 family genes. This annotation was subsequently validated using eggNOG-Mapper (Figure S5; Tables S5 and S6), which produced almost the same results as AgriGO. OsCYP genes showed enrichment in metabolic processes (GO:0008152) and oxidation-reduction processes (GO:0055114) in the biological process category (Figure S5A), while in the molecular category, OsCYPs showed enrichment in catalytic activity (GO:0003824), binding (GO:0005488), electron carrier activity (GO:0009055), oxidoreductase activity (GO:0016491), monooxygenase activity (GO:0004497), tetrapyrrole binding (GO:0046906), and heme binding (GO:0020037) (Figure S5B). Interestingly, no GO enrichments were identified within the cellular category. As a result, these outcomes strongly suggest that the OsCYP71 family genes are pivotal in various metabolic processes in rice.

2.5. Transcriptome Profiling of OsCYP71 Family Genes in Different Tissues and Phytohormone Treatments in Rice

To gain insights into the functions of OsCYP71 genes, we examined their expression patterns across various tissues, developmental stages, and under hormone treatments. We sourced OsCYP71 gene family expression data from the Rice Expression Profile Database (RiceXPro), which we then employed to construct heatmaps. Remarkably, among the 105 OsCYP71 family genes, we identified differential expression across different tissues, developmental stages, and in response to hormone treatments (Figure 7A–C).
For instance, CYP71X4 and CYP71X12 expression was elevated in ovary, embryo, and endosperm tissue, while the expression of CYP71X13P was induced in lemma, palea, and endosperm tissue. Further, the expression of CYP71X7 was highly induced in vegetative, reproductive, ripening leaf blade, vegetative, reproductive leaf sheath, ovary, embryo, and endosperm tissue, whereas the expression of CYP71Z6, CYP71R2P, CYP71R1, and CYP71K12 was highly up-regulated in vegetative, reproductive, ripening leaf, vegetative, and reproductive leaf sheath tissue. CYP71C19P and CYP71Z2 expression were increased in vegetative, reproductive, ripening leaf, vegetative, reproductive leaf sheath, root vegetative, and root reproduction tissue. Additionally, we also examined the expression pattern of OsCYP71 in the shoots and roots under different plant hormone treatments such as auxin, gibberellin (GA), abscisic acid (ABA), cytokinin, jasmonic acid (JA), and brassionsteroid (BRS) (Figure 7B,C). CYP71Q1 expression was elevated in the shoot in the ABA and JA treatments after 3 h, 6 h, and 12 h, while the expression level of CYP71Z4 was induced in the shoot after 3 h, 6 h, and 12 h of GA treatment. The transcript levels of CYP71C16, CYP71Z6, CYP71C19P, CYP71V2, CYP71X10, CYP71W1, and CYP71T1 were increased in the shoot after 3 h, 6 h and 12 h of JA treatment. Further, expression of CYP71X10 increased in the shoot after 3 h, 6 h, and 12 h of BRS treatment, while CYP71X11 was elevated in the shoot after 1 h, 3 h, 6 h, and 12 h of auxin treatment (Figure 7B). The expression of CYP71X12 increased in the root after 1 h, 3 h, and 6 h of cytokinin treatment, whereas CYP71X14, CYP71P1, and CYP71C16 were significantly induced in the root after JA treatment for 30 min, 1 h, and 3 h. Furthermore, CYP71Z4, CYP71W1, CYP71K5, CYP71Z2, CYP71C20, and CYP71Z3 were highly up-regulated in the root after JA treatment for 3 h and 6 h (Figure 7C). Collectively, these results have shown that OsCYP71 gene family members may participate in different developmental processes and responses to phytohormones in rice.

2.6. Gene Diversity Analysis of OsCYP71P6 Alleles

To examine gene variation, four in/del primers were validated in the one hundred and thirty-one different rice varieties. Out of four primers of the OsCYP71P6 gene, two primers were polymorphic (OsCYP71P6-1; OsCYP71P6-4) and the other two were found to be monomorphic in rice varieties. The primer OsCYP71P6-1 has two different allelic variants, having amplicon lengths of 320 bp and 350 bp (Figure S6). Among the 131 rice varieties examined, 113 displayed an amplicon length of 320 bp, while 18 varieties exhibited an amplicon length of 350 bp (Table S7). Similarly, the OsCYP71P6-4 gene also showed two different alleles, with 107 rice varieties displaying amplicon lengths of 380 bp and 24 rice varieties displaying lengths of 400 bp. Further, the in/del OsCYP71P6-1 major allelic frequency was 0.8625, which was relatively higher than that of in/del OsCYP71P6-4 (0.8167); overall, the mean major allelic frequency for both the markers was 0.8396 (Table 2).
Further, mean gene diversity for both the in/dels was found to be 0.2681, while the gene diversity of OsCYP71P6-4 was 0.2992 and that of OsCYP71P6-1 was 0.2370. Additionally, the PIC values of the OsCYP71P6-1 and OsCYP71P6-4 markers were 0.2090 and 0.2545, respectively, with a mean PIC value of 0.2317 (Table 2).

2.7. Grouping of Rice Varieties Based on OsCYP71P6 Alleles

The unweighted pair group method with arithmetic averaging (UPGMA) was employed for distance-based diversity analysis. This analysis revealed the presence of two primary clusters, designated as Cluster A and Cluster B (Figure 8).
Furthermore, Cluster A is subdivided into two branches, namely, A1 and A2, which are further fragmented into several minor clusters. Similarly, Cluster B was also subdivided into two sub clusters (Figure 8). Cluster A encompasses a total of 24 distinct rice varieties, while Cluster B is the largest cluster, comprising 107 different rice varieties. The model-based structure analysis revealed genetic relationships between different rice varieties based on the OsCYP71P6 alleles. The optimal K value was found to be 2 (K = 2), indicating that the 131 rice varieties were grouped into two subpopulations (Figure 9A–C).
Additionally, within subpopulation I, a total of 29 rice varieties were identified, while subpopulation II encompassed 60 rice varieties. Moreover, 42 rice varieties were classified under the admixture category in the analysis. Notably, the analysis employed a threshold value of 60% (Table S8). AMOVA (analysis of molecular variance), based on F-statistics, revealed that 81% of the genetic variation existed between populations, while the remaining 19% of variation was observed within the population (Table 3).

2.8. Descriptive Statistics of Yield-Related Traits for Different Alleles of OsCYP71P6

The descriptive statistics for the seven different traits studied in the one hundred and thirty-one rice varieties are shown in Table 4.
The varieties were grouped based on the alleles of the two polymorphic in/dels of OsCYP71P6. The observed trait values spanned ranges for spikelet number (163.99 to 189.93), single-plant yield (30.71 to 38.48 g), panicle weight (3.29 to 3.61 g), number of productive tillers (10.29 to 11.98), panicle length (25.89 to 27.36 cm), unfilled grain (29.2 to 47.65), filled grain (133.91 to 142.61), and 100 seed weight (2.27 to 2.4 g). The kurtosis values for these traits ranged from −0.83 to 7.58. Notably, among the studied traits, unfilled grain exhibited the highest kurtosis value (7.58). Similarly, the skewness of various traits ranged from −0.78 to 1.85.

2.9. OsCYP71P6 Allelic Difference in Phenotypic Traits

Further, we examined the allelic variation in the OsCYP71P6 gene and associations with phenotypic traits. Among the two OsCYP71P6 primers, the alternate alleles of OsCYP71P6-4 showed statistically significant difference for traits, namely, panicle length, single-plant yield, panicle weight, number of spikelets, and unfilled grain with significant p values of 0.002, 0.005, 0.05, 0.004, and 0.001, respectively (Table 5).
In terms of percent difference in single-plant yield, number of spikelets, panicle length, panicle weight, and unfilled grain for OsCYP71P6-4, the 400 bp allele in/del primer was found to contribute 20.19%, 13.65%, 5.37%, 8.79%, and 36.86% more, respectively, than the 380 bp allele in the studied rice varieties. The average single-plant yield was found to be 30.71 ± 11.38 g and 38.48 ± 13.87 g for alleles of 380 bp and 400 bp lengths, respectively, with a p value of 0.005. Similarly, the mean number of spikelets was found to be 163.99 ± 54.16 and 189.93 ± 43.86 for 380 bp and 400 bp allelles, respectively, and difference was found to be significant (p value: 0.004). The panicle weight trait also showed statistically significant differences between the alleles (p value: 0.05), with the mean values of 3.29 ± 0.95 and 3.61 ± 0.84 for 380 bp and 400 bp alleles, respectively. Likewise, mean panicle length between the alleles was 25.89 ± 2.81 and 27.36 ± 2.26 for 380 bp and 400 bp alleles, respectively, with a p value of 0.002. The 380 bp and 400 bp alleles showed a mean unfilled grain value of 30.08 ± 15.86 and 47.65 ± 28.66, respectively, and a significant p value (0.001). In contrast, the 350 bp allele of the OsCYP71P6-1 primer contributes 17.64% and 14.07% in terms of single-plant yield and number of productive tillers, respectively, relative to the 320 bp allele. Furthermore, the average yield per plant was determined to be 31.21 ± 11.42 g for one allele and 37.90 ± 15.38 g for the other allele of OsCYP71P6-1. This difference in yield was statistically significant, indicated by a p value of 0.03.
The mean phenotypic difference between the two subpopulations identified in the STRUCTURE analysis exhibited significant distinctions for several traits including single-plant yield, number of spikelets, filled grains, and number of productive tillers. Notably, the single-plant yield trait displayed average values of 37.06 ± 15.33 g for subpopulation I and 31.42 ± 10.48 g for subpopulation II, with a significant p value of 0.03. Similarly, the mean number of spikelets was found to be 150.93 ± 28.71 and 185.32 ± 43.63 between the populations, respectively, with a p value of 0.02. In addition, the mean difference for the subpopulations along with admixtures also showed significant difference for single-plant yield, number of spikelets, and filled grains per panicle. Moreover, all the varieties were categorized into four distinct haplotypes based on the amplicon lengths of both primers. These groups were designated as follows: Group-1 (320 and 380 bp), Group-2 (350 and 380 bp), Group-3 (320 and 400 bp), and Group-4 (350 and 400 bp). Among these haplotypes, the single-plant yields observed were 29.81 ± 10.24 g, 37.71 ± 14.41 g, 36.64 ± 16.43 g, and 42.32 ± 11.73 g for the 1st, 2nd, 3rd, and 4th haplotypes, respectively, and the p value for the mean difference between the haplotypes was found to be highly significant (p = 0.005) (Figure 10, Table 5).

2.10. Association of OsCYP71P6 SNPs with the Spikelet Fertility Using Linear Regression Model

We studied the 572 SNPs including in/dels of the OsCYP71P6 gene along with the promoter (Chr12:9577747.9584014) identified among the 1858 genotypes in 3K rice database for their association with spikelet fertility scores using linear regression models. The analysis showed eight SNPs (Table 6) had significant associations, with p values less than 0.05 for spikelet fertility (Chr12: 9581604; Chr12:9582455; Chr12:9582489; Chr12:9582557; Chr12:9582591; Chr12: 9582869; Chr12:9583776; Chr12:9583083; and Chr12:9582921).
Within the set of eight SNPs, a solitary SNP was detected in the coding region, leading to the Ser33Leu amino acid substitution. This change occurred in the span between the signal peptide and the p450 functional domain of the OsCYP71P6 protein. In contrast, all other SNPs were situated in the promoter region of OsCYP71P6. Furthermore, among these eight SNPs, six distinct haplotypes emerged. Notably, Hap2 exhibited a lower percentage (4.6%) of genotypes with a spikelet fertility score of 5, contrasting with the other haplotypes (11.9%). The specific sequence data for the OsCYP71P6 haplotypes can be found in Table S10. Moreover, Hap4 also displayed a reduced count of genotypes (0.8%), with a spikelet fertility score of 7, relative to the other haplotypes (1.7%). The variance analysis unveiled significant distinctions in the number of genotypes across different spikelet fertility scores among the haplotypes (p value: 0.00006) (refer to Tables S10 and S11).

3. Discussion

Serotonin is synthesized in plants from the amino acid tryptophan via decarboxylation to tryptamine, which is then hydroxylated to form serotonin via the shikimate pathway [27]. Tryptophan decarboxylase (TDC) and tryptamine-5-hydroxylase (T-5-H) catalyze these reactions [27]. CYP71A1 is a major gene functioning in the serotonin pathway and is reported to be involved in biotic and abiotic stress tolerance in rice [27,45]. In this work, a total of 105 OsCYP71 family genes were identified in the indica rice genome. These genes were further categorized into twelve distinct subfamilies. Notably, members sharing the same clade exhibited resemblances in terms of gene structures and conserved motifs. In addition, 105 OsCYP71 genes were distributed on 11 chromosomes, and 36 pairs of OsCYP71 involved in gene duplication events were found. The promoter region of OsCYP71 contains a multitude of cis-elements associated with responses to light, hormones, and various stress conditions. Further, transcriptome profiling revealed that a majority of the genes within the OsCYP71 family were responsive to hormonal stimuli and exhibited induction across various tissues and developmental stages in rice. It was discovered that alleles of OsCYP71P6 located in the promoter region play a role in regulating both spikelet fertility and yield-related traits in rice. Furthermore, a non-synonymous substitution adjacent to the signal peptide region of OsCYP71P6 and promoter in/dels were identified as having a notable association with spikelet fertility scores in rice. The implications and the significance of these findings are discussed in detail below.

3.1. Identification and Evolutionary Analysis of the OsCYP71 Gene Family in Rice

The cytochrome P450 (CYP450) gene family plays a critical role in plant growth and developmental processes, nutrition, and detoxification of xenobiotics [1,2,3,5]. In this study, a total of 105 OsCYP71 family genes were identified in the rice genome, compared with Medicago truncatula (59), Arabidopsis thaliana (54), Vitis vinifera (125), Zea mays (55), Sorghum bicolor (87), and Glycine max (53) [5,17,18,40,46,47]. This variation could potentially be attributed to gene duplication events. Phylogenetic relationships revealed that OsCYP71 gene family members were divided into 12 subfamilies (Figure 1). Group VII encompassed a total of 74 members. Conversely, Groups I, II, III, IV, V, VI, VIII, IX, X, XI, and XII contained 0, 8, 5, 0, 13, 2, 3, 0, 0, 0, and 0 members, respectively (Figure S2). Based on the phylogenetic analysis, we assume that the OsCYP71 family has a significant degree of evolutionary expansion in rice. Further, subcellular localization predicts that OsCYP71 gene family members are mainly situated in the endomembrane system. The wide range of Mw and pI of OsCYP71 may decide their functional diversity in different metabolic pathways (Table 1).
Chromosome mapping revealed that the distribution of OsCYP71 genes is uneven across a total of 11 chromosomes (Figure 2A). There are hot spots or gene clusters on Chr2, Chr3, Chr6, Chr8, Chr9, and Chr10 (Figure 2B). The OsCYP71 gene was randomly and unevenly distributed on 12 chromosomes, and WGD or segmental duplication events were detected in each chromosome. Our results suggest that gene duplication affects OsCYP71 chromosome location and gene family expansion depends on sequence duplication, either in WGD or segmental events. Apart from the segmental type, genome-wide duplication events also contribute to the alteration in the count of family members. In general, gene duplication plays an important role in the expansion and evolution of gene families, in which tandem repeats produce gene clusters or hotspots and fragment repeats produce homologous genes [48]. In this study, we identified a total of 36 instances of gene duplication within the OsCYP71 gene family (Figure 3 and Figure S3). Further, different lengths of OsCYP71 may play an important role in diversifying gene functions. The Ka/Ks score for these genes was observed to be less than one, suggesting a robust purifying selection with minor alterations following gene duplication. These findings provide evidence that the OsCYP71 family genes have undergone a conserved evolutionary process. Furthermore, upon conducting an analysis of the exon-intron structure of OsCYP71 genes, it became evident that the quantity of introns exhibited variation across distinct subfamilies (Figure 4). Across all CYP subfamily genes, the count of introns varies from zero to four. However, the majority of these genes typically possess between one and two introns. The maximum four introns were detected in OsCYP71E2, while OsCYP71AD, OsCYP71C3, OsCYP71C4, OsCYP71AB7, OsCYP71W5, OsCYP71U3, OsCYP71T4, OsCYP71T7, OsCYP71T5, OsCYP71T8, OsCYP71T6, and OsCYP71T9 were intronless (Figure 4). Several studies have shown that a number of introns of CYP450 superfamily genes may have been lost during evolutionary processes in plants [13,49,50,51]. Additionally, a conserved motif analysis can offer deeper insights into the functional distinctions among family members. According to the findings from MEME, we identified the presence of motif 10 in OsCYP71 proteins. Motifs 1, 2, 3, 6, 7, and 10 were observed to exhibit strong conservation across the majority of OsCYP71 proteins (Figure 5A,B). The amino acid sequence alignment showed that OsCYP71 proteins consist of the conserved SRS and the heme-binding motifs (Figure S4A). We also predicted the 3D structure of OsCYP71 proteins (Figure S4B). Thus, based on these results, although certain motifs exhibit strong conservation within the OsCYP71 family, diverse subfamilies display unique motifs that potentially participate in specialized functions.

3.2. The OsCYP71 Gene Family Contains Multiple Cis-Regulatory Involved in Plant Developmental Processes

Cis-elements in the promoter region play a key role in the regulation of gene expression [44,52,53,54]. In this study, numerous cis-regulatory elements were identified within the promoter regions of OsCYP71. These encompassed elements responsive to phytohormones, light, cell cycle regulation, circadian rhythms, seed-specific regulation, as well as defense and stress responses (Figure 6A,B). In terms of hormonal response, ABRE, AuxRE, GARE, SARE, and MeJARE were the primary elements (Table S4), and similar results have been observed in other species [12,13,50,51]. Many researchers demonstrated that gene duplication is a key factor in gene family expansion [1,55]. Gene duplication expanded the number of gene family members under evolutionary pressures; further, mutations in these genes may modulate the expression patterns of gene family members [56,57,58,59]. The CYP450 gene family holds a pivotal function in various aspects of plant growth and development, as well as in processes related to nutrition and the detoxification of xenobiotics in plants [1,3,4,5]. In addition, CYP450 has been implicated in diverse metabolic reactions and targets a wide range of biological molecules. These biosynthetic reactions lead to diverse plant hormones, fatty acid conjugates, lignin, secondary metabolites, and numerous defensive compounds [8,9]. It is intriguing to hypothesize that gene expression blueprints are evidence of their biological importance. The expression patterns of OsCYP71 genes were thoroughly investigated across diverse tissues and developmental stages and under various hormone treatments. Our analysis revealed that the 105 OsCYP71-family genes studied exhibited differential expression across various tissues, developmental stages, and in response to hormone treatments (Figure 7A–C). For instance, CYP71X4 and CYP71X12 expression was elevated in ovary, embryo, and endosperm tissue, while the expression of CYP71X13P was induced in lemma, palea, and endosperm tissue. Further, the expression of CYP71X7 was highly induced in vegetative, reproductive, ripening leaf blade, vegetative, reproductive leaf sheath, ovary, embryo and endosperm tissue, whereas the expression of CYP71Z6, CYP71R2P, CYP71R1, and CYP71K12 was highly up-regulated in vegetative, reproductive, ripening leaf blade, and reproductive leaf sheath tissue. In addition, CYP71Q1 expression was elevated in the shoot after 3 h, 6 h, and 12 h of the ABA and JA treatments, while the expression level of CYP71Z4 was induced in the shoot after 3 h, 6 h, and 12 h of GA treatment. The transcript levels of CYP71C16, CYP71Z6, CYP71C19P, CYP71V2, CYP71X10, CYP71W1, and CYP71T1 were induced in the shoot after 3 h, 6 h, and 12 h of JA treatment. Further, expression of CYP71X10 increased in the shoot after 3 h, 6 h, and 12 h of BRS treatment, while CYP71X11 was elevated in the shoot after auxin treatment for 1 h, 3 h, 6 h, and 12 h (Figure 7B). Following cytokinin treatment, the expression of CYP71X12 increased in the root at the 1 h, 3 h, and 6 h time points. Conversely, under JA treatment, CYP71X14, CYP71P1, and CYP71C16 were notably induced in the root after 30 min, 1 h, and 3 h. Furthermore, CYP71Z4, CYP71W1, CYP71K5, CYP71Z2, CYP71C20, and CYP71Z3 were highly up-regulated in the root after JA treatment for 3 h and 6 h (Figure 7C). Similarly, the member of BnCYP86 gene family exhibited expression in different tissues and responses to diverse environmental stresses, as did ABA in Brassica Juncea [13]. The levels of SlCYP71AX and SlCYP77A20 gene expression were elevated in both the green and mature green stages of tomato fruit [50]. Collectively, these results have shown that OsCYP71 gene family members may participate in different developmental processes and responses to phytohormones in rice. Thus, the outcome of this work may provide important insight into deciphering the biological functions of OsCYP71 gene family members in future.

3.3. The Effect of Promoter In/Dels of OsCYP71P6 on Yield-Related Traits

The promoter in/del alleles of OsCYP71P6 exhibited differences of 20.19%, 13.65%, 5.37%, 8.79%, and 36.86% in relation to single-plant yield, number of spikelets per panicle, panicle length, panicle weight, and unfilled grains per panicle, respectively, across various rice varieties. In support of these findings, the previous report by Lu et al. [28] showed the association of the loss-of-function allele of OsCYP71P6 with the yield and number of tillers in rice. This suggests that the specific promoter allele of OsCYP71P6 could be correlated with the gene’s expression level, thereby playing a role in regulating yield-related traits in rice varieties developed for cultivation in India. Previously, it was discovered that promoter insertion/deletions (in/dels) within the malate transporter 9 (Al-MT9) gene in tomato were linked to variations in malate content [60]; the Arabidopsis Fumarase gene (FUM) promoter in/dels regulates carbon assimilation and nitrogen use [61]; the wheat promoter in/del in the elongation factor (TEF-7A) was found to be associated with the grain number per spike [62]. Promoter insertion/deletion (In/Dels) polymorphisms have been linked to heterotic gene expression in hybrid varieties [63]. The promoter in/dels in these examples resulted in altered gene expression and associated phenotypes. Thus, OsCYP71P6 gene expression variation could be related to the identified promoter in/dels, which needs to be further confirmed. Furthermore, the OsCYP71P6 gene participates in the serotonin biosynthesis pathway. Increasing the expression of tryptophan decarboxylase led to elevated serotonin levels. However, this led to a notable decrease in yield due to changes in rice panicle branching and the quantity of spikelets [64]. Additionally, overexpression of rice OsSNAT1 (Serotonin N-Acetyl Transferase-1), which converts serotonin into melatonin, enhanced the number of panicles per plant and yield [65]. This supports the hypothesis that high levels of serotonin could have a negative effect on the yield. Therefore, it is feasible that elevated serotonin levels exert a negative regulatory effect on yield-related traits in rice varieties and targeting OsCYP71P6 promoter insertions/deletions could be a potential strategy to finely adjust both gene expression and serotonin levels in rice, ultimately leading to increased yields.

3.4. The Effect of 3′-UTR In/Dels of OsCYP71P6 on Single-Plant Yield

The 3′-untranslated region (3’-UTR) allele of OsCYP71P6 also showed significant difference for the single-plant yield in rice varieties. The single-plant yield was 17.65% higher in the varieties having the insertion allele (350 bp) of the OsCYP71P6-1 primer. Previously, it was reported in different genes and crops that sequence variation in the 3’-UTR region affects gene expression and was associated with major traits [66,67,68]. Therefore, variation in the 3′-UTR of OsCYP71P6 might influence the expression or stability of the gene’s transcripts, subsequently impacting its related yield traits in rice.

3.5. A Non-Synonymous Substitution Near the Signal Peptide Region of the OsCYP71P6 Gene Regulates Spikelet Fertility

Haplotype 4 of OsCYP71P6 exhibited a notably lower proportion of individuals (0.8%) with elevated spikelet fertility scores (SF7) compared to the other five haplotypes (1.61%). The SNPs leading to the amino acid substitution of Ser33Leu are situated close to the carboxy end of the trans-membrane (TM) signal peptide region within the OsCYP71P6 protein. This indicates that the substitution might be regulating the OsCYP71P6 cellular localization. The eukaryotic CYPs are localized in the membrane, especially the endoplasmic reticulum, and are affected by the membrane properties [69]. Recently, it was found that mutations in the TM region affected the enzymatic function of CYPs [70]. Furthermore, a comparable region within closely related homologs of OsCYP71P6 in rice also displayed sequence variations, including deletions or substitutions affecting serine amino acids (LOC_Os09g26940). This suggests that CYP71 homologs in rice exhibit substantial diversity in their signal peptide regions, and potentially in cellular localizations as well. Consequently, it becomes crucial to determine the functional importance of the amino acid substitution of Ser33Leu in relation to spikelet fertility.

3.6. Gene Diversity of OsCYP71P6 in Rice Varieties

In this study, distance- and model-based analysis of in/dels in the OsCYP71P6 gene grouped the rice varieties into two major groups. In similar findings, two sub-populations were also identified for the Dense and Erect Panicle 1 (DEP1) gene in high-yielding japonica rice varieties [71]. Further, significant differences were observed in single-plant yield and the number of spikelets per panicle (17.6% and 14.1%, respectively) between the varieties categorized within the two distinct sub-populations. This underscores the correlation between genetic variations in the OsCYP71P6 gene and yield-related traits in rice. Of particular significance is the identification of four in/del-based haplotypes within Indian rice varieties, which exhibit substantial differences in single-plant yield, emphasizing that the rice breeding initiative for varietal enhancement possesses ample diversity in the OsCYP71P6 gene that can be strategically harnessed for genetic improvement. In support of this observation, two to three alleles were identified in the major yield-related genes (GS3, qSW5, GS5, Gn1a, and DEP1) in high-yielding varieties grown in Northern China [72]. However, the favorable allele proportion of the OsCYP71P6 gene identified in this study was only ~3%. Therefore, targeted breeding for introgression of the favorable allele of OsCYP71P6 could be attempted to increase the genetic gain for yield in rice. In this regard, the favorable haplotype (Hap4) of OsCYP71P6 identified in four rice varieties (PR106, PR113, HPR2143, Himalaya-1) could be used as a donor for marker-assisted selection for yield improvement, whereas the most common haplotypes present in the rice varieties showed the least mean single-plant yields. Previously, utilizing sequence-based methodologies on major yield-related genes in rice varieties led to the discovery of novel alleles associated with traits related to yield [73]. In comparison, the gene-specific in/dels marker approach taken in this study could also be utilized for the marker-assisted breeding program in rice. Additionally, evaluation of the OsCYP71P6 haplotypes in various rice varieties to determine their influence on traits related to both biotic and abiotic stress tolerance holds significant importance for the effectiveness of breeding efforts and genome editing programs [28]. Nowadays, the rice 3K panel, comprised of sequence information of ~3000 rice genotypes, is used for the identification of the favorable haplotypes of key genes in rice [74]. In this study, information from the 3k database was leveraged to create in/del primers. These primers were developed with the aim of comprehending the diversity of the OsCYP71P6 gene among rice varieties that have been cultivated in India. A similar approach was used in our previous report to identify the UTR specific in/dels in the TPP7 gene for tolerance to germination under submerged conditions [39]. Therefore, this strategy would be immensely valuable for uncovering gene diversity within key genes in the rice genome. Similarly, major gene-specific in/dels have been identified for the yield-related gene Gn1a in rice [68,75]. The superior haplotypes that have been identified could be incorporated into widely grown rice varieties using marker-assisted backcross breeding techniques.

4. Materials and Methods

4.1. Identification and Characterization of OsCYP71 Members in Rice Genome

A hidden Markov Model (HMM) profile of the OsCYP71 conserved domain (PF00067) was obtained from the Pfam database [76] by screening in the rice genome database. OsCYP71 family members were further identified through the NCBI-CDD [77] and SMART databases [78]. An isoelectric point calculator was utilized to analyze the theoretical isoelectric point (PI) and molecular weight (MW) of the OsCYP71 protein [79]. PSORT and BUSCA were used to predict the subcellular localization of OsCYP71 encoded proteins [80,81].

4.2. Phylogenetic Tree, Gene Structure, and Conserved Motif Analysis of the OsCYP71 Family in Rice

Arabidopsis, tomato, and OsCYP71 protein sequences were acquired from Ensembl Plants (https://plants.ensembl.org/index.html, (accessed on 20 June 2023)) and, subsequently, phylogenetic trees were constructed using MEGA 11 [81]. ClustalW was used for sequence alignment, the maximum likelihood method was used for phylogenetic tree construction, and the reliability of the tree obtained was determined using the bootstrap method with 1000 replicates. The exon-intron structure of OsCYP71 genes was visualized and mapped using the Gene Structure Display Server (GSDS) [82]. The conserved motifs of OsCYP71 protein sequences were plotted by the MEME webserver [83]. The Phyre2 web server was used to create the 3D structure of OsCYP71 [84].

4.3. Chromosome Localization, Gene Replication, Cis-Regulatory Elements, GO Enrichment, and Expression Analysis

The acquisition of chromosome localization information for OsCYP71 genes was carried out through Ensembl Plants (http://plants.ensembl.org/biomart/martview, (accessed on 20 June 2023), aiming to facilitate their placement on the respective chromosomes. The OsCYP71 gene family members were mapped using PhenoGram [85]. Gene replication and Ka/Ks values were analyzed using MCScan tools [86] and TBtools [87]. The 1500 bp sequence upstream of the OsCYP71 genes was used for promoter element analysis by the PlantCARE webserver [88]. GO enrichment of OsCYP71 proteins was performed using the singular enrichment analysis (SEA) function of a network-based agriGO program [89] Tissue-specific expression values for the 105 OsCYP71 genes were extracted from the RiceXPro database (http://ricexpro.dna.affrc.go.jp, (accessed on 28 June 2023)) and, subsequently, heatmaps were generated using ClustVis [90].

4.4. Phenotyping for Different Traits

The study was carried out at ICAR National Rice Research Institute, Cuttack in January 2021. A total of 131 released rice varieties were considered for the phenotyping data analysis (Table S7). Approximately a hundred seeds of the varieties were line sown in the nursery and transplanted in the puddled field conditions, with two lines per replication and each line consisting of fifteen seedlings. Additionally, two replications and alpha lattice designs [91] to reduce the variability in field experiments were maintained for the evaluation of the yield-related traits. The standard management practices of application of fertilizer and irrigation management were followed and at the physiological maturity stage, the number of productive tillers (nos.) was directly measured in three plants per line in the field. Further, at the crop’s maturity stage, various parameters including panicle length (in centimeters), panicle weight (in grams), spikelet count, both filled and unfilled grain quantities, seed weight (in grams), and single-plant yield (in grams) were assessed. This analysis involved the harvesting of three plants per line. The collected panicles were sun-dried for a duration of one week before being utilized for the assessment of panicle-related characteristics. The methodologies for measuring these traits were consistent with previously established protocols [92,93]. Briefly, panicle length was measured manually using a measuring scale from the top three panicles per plant. Further, the weight of these panicles was measured using a weighing machine. Additionally, the top three panicles per plant were also used for manually counting the number of spikelets per panicle along with filled and unfilled grains. Then, a hundred grains per line were manually counted and weighed for 100 g seed weight. Furthermore, the panicles of individual plants were subjected to threshing, and the filled spikelets were weighed to determine the yield per single plant. In parallel, young leaves were gathered for DNA extraction, rapidly frozen using liquid nitrogen, and subsequently preserved at a temperature of −80 °C for future investigations.

4.5. Development of Gene Specific Polymorphic Variants-Insertion/Deletions (GPV-In/Dels)

Insertion/deletions present in the OsCYP71P6 (CYP71A1, LOC_Os12g16720) gene, including the promoter, were identified from the SNP Seek database of rice (https://snp-seek.irri.org/, (accessed on 29 June 2023)). The in/del variants, present in approximately 3000 genotypes within the SNP Seek database, were detected by aligning against the Nipponbare reference genome. These variants were specific to certain genes. As a result of this development, the marker was designated as “Gene-Specific Polymorphic Variants-In/Dels” (GPV-In/Dels). Further, the CYP71A1 gene sequence of Nipponbare was retrieved from the RGAP database (http://rice.plantbiology.msu.edu/, (accessed 29 June 2023)) and utilized for designing the primers flanking the selected in/dels of size > 10 bp. The details of the primer sequence are given in Tables S2 and S8. In addition, the SNPs and in/dels, totaling around 572, that were discovered within the OsCYP71P6 gene were subjected to analysis along with the spikelet fertility quality scores (ranging from 1 to 7) available in the 3 K rice database. This analysis involved investigating the associations between variants and traits through a linear regression model. The ‘lm’ function within the R software, specifically R Version 3.6.0, was employed for this purpose [94].

4.6. Experimental Validation of OsCYP71P6 In/Dels

Samples of leaves from various rice varieties were gathered to validate the newly created in/del markers. The CTAB (cetyl trimethyl ammonium bromide) technique was employed to extract genomic DNA from these leaf samples [92]. Using agarose gel electrophoresis, the purity of the DNA was determined (0.8 percent). For PCR amplification, genomic DNA was diluted with nuclease-free water to a working concentration (50 ng/µL). Initially, all four gene-specific in/del markers were employed to genotype rice varieties, facilitating an analysis of genetic diversity. The PCR was carried out in a 10 μL reaction volume containing 1 μL of template DNA, 1 μL of 10× buffer (1.5 mM Mg in 1×), 1.0 μL of dNTP (2.5 mM), 1 μL of each in/del primer (0.2 μM), and 0.2 U of Taq polymerase (Kapa Biosystem, Cape Town, South Africa), while the volume was made using double-distilled H2O. The PCR program includes an initial denaturation for 3 min at 95 °C, which was followed by 35 cycles of denaturation for 30 s at 95 °C, annealing for 45 s at 55 °C, and an extension for 1 min at 72 °C, with a final extension for 10 min at 72 °C using a thermal master cycler (Eppendorf, Hamburg, Germany). The PCR products were resolved using a 4.0 percent agarose gel and recorded using a UV gel documentation system (Bio-Rad, Hercules, CA, USA) (Figure S6). The alleles of the in/del marker were assessed manually, taking into account their amplicon length across various rice cultivars.

4.7. Diversity Analysis

The alleles identified in 131 rice varieties for OsCYP71P6 gene in/del markers were used for gene diversity, cluster analysis, sub-population structures, and analysis of molecular variance (AMOVA). Gene diversity and cluster analysis were performed using power marker software [95]. Further, AMOVA was performed using the GenALEx V 6.5 tool using amplicon length allelic data as variables [96]. The STRUCTURE software was utilized to analyze population sub-structure, while Structure Harvester was employed to ascertain the optimal number for interpreting the results [97].

5. Conclusions

In this study, we identified 105 OsCYP71 genes within the indica rice genome. These genes were subsequently classified into 12 distinct subfamilies based on shared characteristics. Notably, genes within the same subfamily exhibited similar gene structures and conserved motifs. Additionally, the distribution of these 105 OsCYP71 genes spanned across 11 chromosomes, with 36 instances of OsCYP71 gene duplication events. The promoter regions of OsCYP71 genes were found to harbor a substantial number of cis-elements associated with light responsiveness, hormone signaling (including Auxin, cytokinin, GA, ABA, MeJA, JA, BRS, and SA), and various stresses such as drought and low temperature. Transcriptome profiling further revealed that a majority of genes within this family demonstrated responsiveness to hormones and were induced across diverse tissues and developmental stages in rice. Employing linear regression models, we identified eight promoters along with a gene variant (Ser33Leu) within OsCYP71P6 that exhibited a significant association with spikelet fertility. Furthermore, the allelic effects of different OsCYP71P6 alleles, identified through in/dels polymorphism in 131 rice varieties, were validated for their impact on yield-related traits. Our investigations also revealed that the OsCYP71P6 gene plays a role in the regulation of spikelet count, filled grains, single-plant yield, panicle length, and panicle weight in various rice varieties. These findings serve as a robust foundation for deeper exploration of the functions of OsCYP71-family genes in a wide array of biological processes. Additionally, the outcomes of our study underscore the potential influence of promoter allelic variation and the Ser33Leu amino acid substitution within the OsCYP71P6 gene on yield-related traits in rice. As a result, the promoter variants of OsCYP71P6 that we have identified hold promise for utilization in efforts aimed at enhancing rice yield.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/plants12173035/s1: Figure S1: The molecular mass (kDa) and isoelectric point of plots of OsCYP71 genes; Figure S2: Dispersal of OsCYP71s in a different group of the phylogenetic tree; Figure S3: Evolutionary analysis of OsCYP71 genes. A phylogenetic tree was produced using MEGA7 with the NJ method and 1000 bootstrap replications. A black asterisk denotes the duplicated gene pairs; Figure S4: Amino acid sequence alignment and three-dimensional structure of the OsCYP71. A. The SRS and heme-binding domain is underlined in red color. B. Predicted 3D structures of OsCYP71 proteins; Figure S5: Gene ontology term distribution in the OsCYP71 gene family predicted using AgriGO A. Biological and B. Molecular category; Figure S6: Gel image analysis of the amplified products of OsCYP71P6-1 and OsCYP71P6-4 alleles in agarose gel (4%). a) Alleles of the OsCYP71P6-1 in rice varieties b) alleles of OsCYP71P6-4 in rice varieties. V—Varieties. The name of the varieties is given in Table S8. Table S1: OsCYP genomic, CDS protein and promoter sequence; Table S2: CYP proteins from Arabidopsis, rice and tomato were used to generate a phylogenetic tree; Table S3: The ratio of Ka/Ks and allocation of replicated rice CYP genes; Table S4: Cis-regulatory elements are identified in the OsCYP gene promoter region; Table S5: Significant Go term predicted in OsCYP gene family by AgriGo analysis; Table S6 OsCYP gene annotation using eggNOGmapper; Table S7: Mean values of yield-related traits and amplicon length alleles/polymorphism of OsCYP71P6 in/del primers in rice varieties; Table S8: In/Del Primer sequences used in the study; Table S9: Structure analysis and inferred clusters of the two sub-populations in the rice varieties for OsCYP71P6 In/Del variants; Table S10: Proportion of genotypes in six different haplotypes from nine associated SNPs in OsCYP71P6 gene retrieved from rice 3K database; Table S11: Analysis of variance (ANOVA) for the proportion of genotypes in different haplotypes of OsCYP71P6 in rice.

Author Contributions

C.P. and M.S.K. designed and wrote the manuscript; C.P. acquired funding; C.P. and M.S.K. supervised the study; B.S. and I.N.: genotyping, field data collection, and primary analysis; K.K.S.: statistical analysis, manuscript editing; H.N.S.: data finalization, assisting in field experiments; C.B.: bioinformatics analysis and preparation of primary draft of manuscript; S.R.P.: data interpretation and manuscript editing; J.L.K.: analysis of data, finalization of manuscript; S.K.D.: manuscript; S.-M.C., M.H.S., S.A. and S.S. provided valuable feedback to this study. All authors have read and agreed to the published version of the manuscript.

Funding

We sincerely thank the Indian Council of Agricultural Research, New Delhi (ICAR) and National Agricultural Science Fund (NASF), ICAR, New Delhi for providing the funding support to carry out the study. Also, the study was supported by the Researchers Supporting Project number (RSP2023R194), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is available in the manuscript and in the Supplementary Materials.

Acknowledgments

We also thank Amaresh Kumar Nayak, Director, ICAR-NRRI, Cuttack for providing the facilities required to carry out the research work. The first authors acknowledge and thank the Department of Science and Technology (DST), New Delhi for providing the INSPIRE fellowship to pursue the Ph.D. doctoral research work. The authors would like to extend their sincere appreciation to the Researchers Supporting Project number (RSP2023R194), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mizutani, M.; Ohta, D. Diversification of P450 genes during land plant evolution. Annu. Rev. Plant Biol. 2010, 61, 291–315. [Google Scholar] [CrossRef]
  2. Nelson, D.R. The cytochrome p450 homepage. Hum. Genom. 2009, 4, 59–65. [Google Scholar] [CrossRef] [PubMed]
  3. Yu, J.; Tehrim, S.; Wang, L.; Dossa, K.; Zhang, X.; Ke, T.; Liao, B. Evolutionary history and functional divergence of the cytochrome P450 gene superfamily between Arabidopsis thaliana and Brassica species uncover effects of whole genome and tandem duplications. BMC Genom. 2017, 18, 733. [Google Scholar] [CrossRef] [PubMed]
  4. Xiong, R.; He, T.; Wang, Y.; Liu, S.; Gao, Y.; Yan, H.; Xiang, Y. Genome and transcriptome analysis to understand the role diversification of cytochrome P450 gene under excess nitrogen treatment. BMC Plant Biol. 2021, 21, 447. [Google Scholar] [CrossRef] [PubMed]
  5. Jiu, S.; Xu, Y.; Wang, J.; Wang, L.; Liu, X.; Sun, W.; Sabir, I.a.; Ma, C.; Xu, W.; Wang, S. The cytochrome P450 monooxygenase inventory of grapevine (Vitis vinifera L.): Genome-wide identification, evolutionary characterization and expression analysis. Front. Genet. 2020, 11, 44. [Google Scholar] [CrossRef] [PubMed]
  6. Ohmura, E.; Nakamura, T.; Tian, R.H.; Yahara, S.; Yoshimitsu, H.; Nohara, T. 26-Aminocholestanol derivative, a novel key intermediate of steroidal alkaloids, from Solanum abutiloides. Tetrahedron Lett. 1995, 36, 8443–8444. [Google Scholar] [CrossRef]
  7. Guttikonda, S.K.; Trupti, J.; Bisht, N.C.; Chen, H.; An, Y.-Q.C.; Pandey, S.; Xu, D.; Yu, O. Whole genome co-expression analysis of soybean cytochrome P450 genes identifies nodulation-specific P450 monooxygenases. BMC Plant Biol. 2010, 10, 243. [Google Scholar] [CrossRef]
  8. Schuler, M.A.; Werck-Reichhart, D. Functional genomics of P450s. Annu. Rev. Plant Biol. 2003, 54, 629–667. [Google Scholar] [CrossRef]
  9. Tang, B.; Cheng, Y.; Li, Y.; Li, W.; Ma, Y.; Zhou, Q.; Lu, K. Adipokinetic hormone regulates cytochrome P450-mediated imidacloprid resistance in the brown planthopper, Nilaparvatalugens. Chemosphere 2020, 259, 127490. [Google Scholar] [CrossRef]
  10. Ma, B.; Luo, Y.; Jia, L.; Qi, X.; Zeng, Q.; Xiang, Z.; He, N. Genome-wide identification and expression analyses of cytochrome P450 genes in mulberry (Morus notabilis). J. Integr. Plant Biol. 2014, 56, 887–901. [Google Scholar] [CrossRef]
  11. Ren, J.; Yang, L.; Li, Q.; Zhang, Q.; Sun, C.; Liu, X.; Yang, N. Global investigation of cytochrome P450 genes in the chicken genome. Genes 2019, 10, 617. [Google Scholar] [CrossRef]
  12. Sun, K.; Fang, H.; Chen, Y.; Zhuang, Z.; Chen, Q.; Shan, T.; Khan, M.K.R.; Zhang, J.; Wang, B. Genome-wide analysis of the cytochrome P450 gene family involved in salt tolerance in Gossypium hirsutum. Front. Plant Sci. 2021, 12, 685054. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, Z.; Zhang, Y.; Song, M.; Tang, X.; Huang, S.; Linhu, B.; Jin, P.; Guo, W.; Li, F.; Xing, L. Genome-Wide Identification of the Cytochrome P450 Superfamily Genes and Targeted Editing of BnCYP704B1 Confers Male Sterility in Rapeseed. Plants 2023, 12, 365. [Google Scholar] [CrossRef] [PubMed]
  14. Xia, Y.; Yang, J.; Ma, L.; Yan, S.; Pang, Y. Genome-wide identification and analyses of drought/salt-responsive cytochrome P450 genes in Medicago truncatula. Int. J. Mol. Sci. 2021, 22, 9957. [Google Scholar] [CrossRef]
  15. Qin, P.; Zheng, H.; Tao, Y.; Zhang, Y.; Chu, D. Genome-Wide Identification and Expression Analysis of the Cytochrome P450 Gene Family in Bemisiatabaci MED and Their Roles in the Insecticide Resistance. Int. J. Mol. Sci. 2023, 24, 5899. [Google Scholar] [CrossRef]
  16. Durst, F.; Nelson, D.R. Diversity and evolution of plant P450 and P450-reductases. Drug Metab. Durg Interat. 1995, 12, 189–206. [Google Scholar] [CrossRef] [PubMed]
  17. Paquette, S.M.; Bak, S.; Feyereisen, R. Intron–exon organization and phylogeny in a large superfamily, the paralogous cytochrome P450 genes of Arabidopsis thaliana. DNA Cell Biol. 2000, 19, 307–317. [Google Scholar] [CrossRef] [PubMed]
  18. Nelson, D.R.; Schuler, M.A.; Paquette, S.M.; Werck-Reichhart, D.; Bak, S. Comparative genomics of rice and Arabidopsis. Analysis of 727 cytochrome P450 genes and pseudogenes from a monocot and a dicot. Plant Physiol. 2004, 135, 756–772. [Google Scholar] [CrossRef] [PubMed]
  19. Nelson, D.R. Cytochrome P450 Nomenclature, 2004. Methods Mol Biol. 2006, 320, 1–10. [Google Scholar]
  20. Bak, S.; Beisson, F.; Bishop, G.; Hamberger, B.; Höfer, R.; Paquette, S.; Werck-Reichhart, D. Cytochromes P450. Arab. Book/Am. Soc. Plant Biol. 2011, 9, e0144. [Google Scholar] [CrossRef]
  21. Du, H.; Huang, Y.; Tang, Y. Genetic and metabolic engineering of isoflavonoid biosynthesis. Appl. Microbiol. Biotechnol. 2010, 86, 1293–1312. [Google Scholar] [CrossRef]
  22. Du, Y.; Chu, H.; Chu, I.K.; Lo, C. CYP93G2 is a flavanone 2-hydroxylase required for C-glycosylflavone biosynthesis in rice. Plant Physiol. 2010, 154, 324–333. [Google Scholar] [CrossRef]
  23. Sawada, Y.; Kinoshita, K.; Akashi, T.; Aoki, T.; Ayabe, S.i. Key amino acid residues required for aryl migration catalysed by the cytochrome P450 2-hydroxyisoflavanone synthase. Plant J. 2002, 31, 555–564. [Google Scholar] [CrossRef] [PubMed]
  24. Cooper, L.; Doss, R.; Price, R.; Peterson, K.; Oliver, J. Application of Bruchin B to pea pods results in the up-regulation of CYP93C18, a putative isoflavone synthase gene, and an increase in the level of pisatin, an isoflavone phytoalexin. J. Exp. Bot. 2005, 56, 1229–1237. [Google Scholar] [CrossRef] [PubMed]
  25. Waki, T.; Yoo, D.; Fujino, N.; Mameda, R.; Denessiouk, K.; Yamashita, S.; Motohashi, R.; Akashi, T.; Aoki, T.; Ayabe, S.-i. Identification of protein–protein interactions of isoflavonoid biosynthetic enzymes with 2-hydroxyisoflavanone synthase in soybean (Glycine max (L.) Merr.). Biochem. Biophys. Res. Commun. 2016, 469, 546–551. [Google Scholar] [CrossRef]
  26. Chang, Z.; Wang, X.; Wei, R.; Liu, Z.; Shan, H.; Fan, G.; Hu, H. Functional expression and purification of CYP93C20, a plant membrane-associated cytochrome P450 from Medicago truncatula. Protein Expr. Purif. 2018, 150, 44–52. [Google Scholar] [CrossRef] [PubMed]
  27. Erland, L.A.; Turi, C.E.; Saxena, P.K. Serotonin in plants: Origin, functions, and implications. Serotonin 2019, 23–46. [Google Scholar] [CrossRef]
  28. Lu, H.-P.; Luo, T.; Fu, H.-W.; Wang, L.; Tan, Y.-Y.; Huang, J.-Z.; Wang, Q.; Ye, G.-Y.; Gatehouse, A.M.; Lou, Y.-G. Resistance of rice to insect pests mediated by suppression of serotonin biosynthesis. Nat. Plants 2018, 4, 338–344. [Google Scholar] [CrossRef]
  29. Khush, G.S. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol. Biol. 2005, 59, 1–6. [Google Scholar] [CrossRef]
  30. Kesawat, M.S.; Das, B.K.; Bhaganagare, G.R.; Manorama. Genome-wide identification, evolutionary and expression analyses of putative Fe–S biogenesis genes in rice (Oryza sativa). Genome 2012, 55, 571–583. [Google Scholar] [CrossRef]
  31. Sakamoto, T.; Matsuoka, M. Identifying and exploiting grain yield genes in rice. Curr. Opin. Plant Biol. 2008, 11, 209–214. [Google Scholar] [CrossRef] [PubMed]
  32. Li, X.; Chen, Z.; Zhang, G.; Lu, H.; Qin, P.; Qi, M.; Yu, Y.; Jiao, B.; Zhao, X.; Gao, Q. Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions. Sci. China Life Sci. 2020, 63, 1688–1702. [Google Scholar] [CrossRef] [PubMed]
  33. Jiao, Y.; Wang, Y.; Xue, D.; Wang, J.; Yan, M.; Liu, G.; Dong, G.; Zeng, D.; Lu, Z.; Zhu, X. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat. Genet. 2010, 42, 541–544. [Google Scholar] [CrossRef] [PubMed]
  34. Li, W.; Chern, M.; Yin, J.; Wang, J.; Chen, X. Recent advances in broad-spectrum resistance to the rice blast disease. Curr. Opin. Plant Biol. 2019, 50, 114–120. [Google Scholar] [CrossRef] [PubMed]
  35. Huang, L.-Y.; Li, X.-X.; Zhang, Y.-B.; Fahad, S.; Fei, W. dep1 improves rice grain yield and nitrogen use efficiency simultaneously by enhancing nitrogen and dry matter translocation. J. Integr. Agric. 2022, 21, 3185–3198. [Google Scholar] [CrossRef]
  36. Han, B.; Xue, Y. Genome-wide intraspecific DNA-sequence variations in rice. Curr. Opin. Plant Biol. 2003, 6, 134–138. [Google Scholar] [CrossRef]
  37. Miyashita, N.T.; Yoshida, K.; Ishii, T. DNA variation in the metallothionein genes in wild rice Oryza rufipogon: Relationship between DNA sequence polymorphism, codon bias and gene expression. Genes Genet. Syst. 2005, 80, 173–183. [Google Scholar] [CrossRef]
  38. Alexandrov, N.; Tai, S.; Wang, W.; Mansueto, L.; Palis, K.; Fuentes, R.R.; Ulat, V.J.; Chebotarov, D.; Zhang, G.; Li, Z. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 2015, 43, D1023–D1027. [Google Scholar] [CrossRef]
  39. Chidambaranathan, P.; Sabarinathan, S.; Sanghamitra, P.; Dash, G.K.; Lenka, D.; Balasubramania, C.; KJ, P.; Sahoo, R.K.; Samantaray, S.; BN, D. Haplogenic Quantitative Effects Regulate Flooded Germination, Subsequent Water Deficit Stress, and Recovery in Direct Seeded Rice. Authorea 2022. [Google Scholar] [CrossRef]
  40. Xu, W.; Bak, S.; Decker, A.; Paquette, S.M.; Feyereisen, R.; Galbraith, D.W. Microarray-based analysis of gene expression in very large gene families: The cytochrome P450 gene superfamily of Arabidopsis thaliana. Gene 2001, 272, 61–74. [Google Scholar] [CrossRef]
  41. Xie, M.-M.; Gong, D.-P.; Li, F.-X.; Liu, G.-S.; Sun, Y.-H. Genome-wide analysis of cytochrome P450 monooxygenase genes in the tobacco. Yi Chuan Hered. 2013, 35, 379–387. [Google Scholar] [CrossRef] [PubMed]
  42. Babu, P.R.; Rao, K.V.; Reddy, V.D. Structural organization and classification of cytochrome P450 genes in flax (Linum usitatissimum L.). Gene 2013, 513, 156–162. [Google Scholar] [CrossRef] [PubMed]
  43. Yu, J.; Hu, S.; Wang, J.; Wong, G.K.-S.; Li, S.; Liu, B.; Deng, Y.; Dai, L.; Zhou, Y.; Zhang, X. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 2002, 296, 79–92. [Google Scholar] [CrossRef] [PubMed]
  44. Kesawat, M.S.; Kherawat, B.S.; Katara, J.L.; Parameswaran, C.; Misra, N.; Kumar, M.; Chung, S.-M.; Alamri, S.; Siddiqui, M.H. Genome-wide analysis of proline-rich extensin-like receptor kinases (PERKs) gene family reveals their roles in plant development and stress conditions in Oryza sativa L. Plant Sci. 2023, 334, 111749. [Google Scholar] [CrossRef] [PubMed]
  45. Fujiwara, T.; Maisonneuve, S.; Isshiki, M.; Mizutani, M.; Chen, L.; Wong, H.L.; Kawasaki, T.; Shimamoto, K. Sekiguchi lesion gene encodes a cytochrome P450 monooxygenase that catalyzes conversion of tryptamine to serotonin in rice. J. Biol. Chem. 2010, 285, 11308–11313. [Google Scholar] [CrossRef]
  46. Fang, Y.; Jiang, J.; Du, Q.; Luo, L.; Li, X.; Xie, X. Cytochrome P450 superfamily: Evolutionary and functional divergence in sorghum (Sorghum bicolor) stress resistance. J. Agric. Food Chem. 2021, 69, 10952–10961. [Google Scholar] [CrossRef]
  47. Khatri, P.; Wally, O.; Rajcan, I.; Dhaubhadel, S. Comprehensive analysis of cytochrome P450 monooxygenases reveals insight into their role in partial resistance against Phytophthora sojae in soybean. Front. Plant Sci. 2022, 13, 862314. [Google Scholar] [CrossRef]
  48. Hughes, A.L. The evolution of functionally novel proteins after gene duplication. Proc. R. Soc. B Biol. Sci. 1994, 256, 119–124. [Google Scholar]
  49. Lin, H.; Zhu, W.; Silva, J.C.; Gu, X.; Buell, C.R. Intron gain and loss in segmentally duplicated genes in rice. Genome Biol. 2006, 7, R41. [Google Scholar] [CrossRef]
  50. Vasav, A.; Barvkar, V. Phylogenomic analysis of cytochrome P450 multigene family and their differential expression analysis in Solanum lycopersicum L. suggested tissue specific promoters. BMC Genom. 2019, 20, 116. [Google Scholar] [CrossRef]
  51. Du, H.; Ran, F.; Dong, H.-L.; Wen, J.; Li, J.-N.; Liang, Z. Genome-wide analysis, classification, evolution, and expression analysis of the cytochrome P450 93 family in land plants. PLoS ONE 2016, 11, e0165020. [Google Scholar] [CrossRef] [PubMed]
  52. Kesawat, M.S.; Kherawat, B.S.; Ram, C.; Singh, A.; Dey, P.; Gora, J.S.; Misra, N.; Chung, S.-M.; Kumar, M. Genome-Wide Identification and Expression Profiling of Aconitase Gene Family Members Reveals Their Roles in Plant Development and Adaptation to Diverse Stress in Triticum aestivum L. Plants 2022, 11, 3475. [Google Scholar] [CrossRef] [PubMed]
  53. Kesawat, M.S.; Kherawat, B.S.; Singh, A.; Dey, P.; Routray, S.; Mohapatra, C.; Saha, D.; Ram, C.; Siddique, K.H.; Kumar, A. Genome-wide analysis and characterization of the proline-rich extensin-like receptor kinases (PERKs) gene family reveals their role in different developmental stages and stress conditions in wheat (Triticum aestivum L.). Plants 2022, 11, 496. [Google Scholar] [CrossRef] [PubMed]
  54. Hapgood, J.P.; Riedemann, J.; Scherer, S.D. Regulation of gene expression by GC-rich DNA Cis-elements. Cell Biol. Int. 2001, 25, 17–31. [Google Scholar] [CrossRef] [PubMed]
  55. Freeling, M. Bias in plant gene content following different sorts of duplication: Tandem, whole-genome, segmental, or by transposition. Annu. Rev. Plant Biol. 2009, 60, 433–453. [Google Scholar] [CrossRef] [PubMed]
  56. Heidari, P.; Abdullah; Faraji, S.; Poczai, P. Magnesium transporter gene family: Genome-wide identification and characterization in Theobroma cacao, Corchorus capsularis, and Gossypium hirsutum of family Malvaceae. Agronomy 2021, 11, 1651. [Google Scholar] [CrossRef]
  57. Heidari, P.; Puresmaeli, F.; Mora-Poblete, F. Genome-wide identification and molecular evolution of the magnesium transporter (MGT) gene family in Citrullus lanatus and Cucumis sativus. Agronomy 2022, 12, 2253. [Google Scholar] [CrossRef]
  58. Kesawat, M.S.; Kherawat, B.S.; Singh, A.; Dey, P.; Kabi, M.; Debnath, D.; Saha, D.; Khandual, A.; Rout, S.; Manorama; et al. Genome-wide identification and characterization of the brassinazole-resistant (BZR) gene family and its expression in the various developmental stage and stress conditions in wheat (Triticum aestivum L.). Int. J. Mol. Sci. 2021, 22, 8743. [Google Scholar] [CrossRef]
  59. Kumar, M.; Kherawat, B.S.; Dey, P.; Saha, D.; Singh, A.; Bhatia, S.K.; Ghodake, G.S.; Kadam, A.A.; Kim, H.-U.; Manorama; et al. Genome-wide identification and characterization of PIN-FORMED (PIN) gene family reveals role in developmental and various stress conditions in Triticum aestivum L. Int. J. Mol. Sci. 2021, 22, 7396. [Google Scholar] [CrossRef]
  60. Ye, J.; Wang, X.; Hu, T.; Zhang, F.; Wang, B.; Li, C.; Yang, T.; Li, H.; Lu, Y.; Giovannoni, J.J. An InDel in the promoter of Al-ACTIVATED MALATE TRANSPORTER9 selected during tomato domestication determines fruit malate contents and aluminum tolerance. Plant Cell 2017, 29, 2249–2268. [Google Scholar] [CrossRef]
  61. Pracharoenwattana, I.; Zhou, W.; Keech, O.; Francisco, P.B.; Udomchalothorn, T.; Tschoep, H.; Stitt, M.; Gibon, Y.; Smith, S.M. Arabidopsis has a cytosolic fumarase required for the massive allocation of photosynthate into fumaric acid and for rapid plant growth on high nitrogen. Plant J. 2010, 62, 785–795. [Google Scholar] [CrossRef] [PubMed]
  62. Zheng, J.; Liu, H.; Wang, Y.; Wang, L.; Chang, X.; Jing, R.; Hao, C.; Zhang, X. TEF-7A, a transcript elongation factor gene, influences yield-related traits in bread wheat (Triticum aestivum L.). J. Exp. Bot. 2014, 65, 5351–5365. [Google Scholar] [CrossRef]
  63. Zhang, H.-Y.; He, H.; Chen, L.-B.; Li, L.; Liang, M.-Z.; Wang, X.-F.; Liu, X.-G.; He, G.-M.; Chen, R.-S.; Ma, L.-G. A genome-wide transcription analysis reveals a close correlation of promoter INDEL polymorphism and heterotic gene expression in rice hybrids. Mol. Plant 2008, 1, 720–731. [Google Scholar] [CrossRef] [PubMed]
  64. Kanjanaphachoat, P.; Wei, B.-Y.; Lo, S.-F.; Wang, I.-W.; Wang, C.-S.; Yu, S.-M.; Yen, M.-L.; Chiu, S.-H.; Lai, C.-C.; Chen, L.-J. Serotonin accumulation in transgenic rice by over-expressing tryptophan decarboxlyase results in a dark brown phenotype and stunted growth. Plant Mol. Biol. 2012, 78, 525–543. [Google Scholar] [CrossRef]
  65. Lee, K.; Back, K. Overexpression of rice serotonin N-acetyltransferase 1 in transgenic rice plants confers resistance to cadmium and senescence and increases grain yield. J. Pineal Res. 2017, 62, e12392. [Google Scholar] [CrossRef] [PubMed]
  66. Eveland, A.L.; McCarty, D.R.; Koch, K.E. Transcript profiling by 3′-untranslated region sequencing resolves expression of gene families. Plant Physiol. 2008, 146, 32–44. [Google Scholar] [CrossRef]
  67. Vignesh, M.; Nepolean, T.; Hossain, F.; Singh, A.; Gupta, H. Sequence variation in 3′ UTR region of crtRB1 gene and its effect on β-carotene accumulation in maize kernel. J. Plant Biochem. Biotechnol. 2013, 22, 401–408. [Google Scholar] [CrossRef]
  68. Kim, S.-R.; Ramos, J.; Ashikari, M.; Virk, P.S.; Torres, E.A.; Nissila, E.; Hechanova, S.L.; Mauleon, R.; Jena, K.K. Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L. Rice 2016, 9, 12. [Google Scholar] [CrossRef]
  69. Brignac-Huber, L.M.; Park, J.W.; Reed, J.R.; Backes, W.L. Cytochrome P450 organization and function are modulated by endoplasmic reticulum phospholipid heterogeneity. Drug Metab. Dispos. 2016, 44, 1859–1866. [Google Scholar] [CrossRef]
  70. Mustafa, G.; Nandekar, P.P.; Camp, T.J.; Bruce, N.J.; Gregory, M.C.; Sligar, S.G.; Wade, R.C. Influence of transmembrane helix mutations on cytochrome P450-membrane interactions and function. Biophys. J. 2019, 116, 419–432. [Google Scholar] [CrossRef]
  71. Zhao, M.; Sun, J.; Xiao, Z.; Cheng, F.; Xu, H.; Tang, L.; Chen, W.; Xu, Z.; Xu, Q. Variations in DENSE AND ERECT PANICLE 1 (DEP1) contribute to the diversity of the panicle trait in high-yielding japonica rice varieties in northern China. Breed. Sci. 2016, 66, 599–605. [Google Scholar] [CrossRef]
  72. Dan, L.; WANG, J.-y.; WANG, X.-x.; YANG, X.-l.; Jian, S.; CHEN, W.-f. Genetic diversity and elite gene introgression reveal the japonica rice breeding in northern China. J. Integr. Agric. 2015, 14, 811–822. [Google Scholar]
  73. Vemireddy, L.R.; Kadambari, G.; Reddy, G.E.; Kola, V.S.R.; Ramireddy, E.; Puram, V.R.R.; Badri, J.; Eslavath, S.N.; Bollineni, S.N.; Naik, B.J. Uncovering of natural allelic variants of key yield contributing genes by targeted resequencing in rice (Oryza sativa L.). Sci. Rep. 2019, 9, 8192. [Google Scholar] [CrossRef]
  74. Abbai, R.; Singh, V.K.; Nachimuthu, V.V.; Sinha, P.; Selvaraj, R.; Vipparla, A.K.; Singh, A.K.; Singh, U.M.; Varshney, R.K.; Kumar, A. Haplotype analysis of key genes governing grain yield and quality traits across 3K RG panel reveals scope for the development of tailor-made rice with enhanced genetic gains. Plant Biotechnol. J. 2019, 17, 1612–1622. [Google Scholar] [CrossRef] [PubMed]
  75. Yan, C.-J.; Yan, S.; Yang, Y.-C.; Zeng, X.-H.; Fang, Y.-W.; Zeng, S.-Y.; Tian, C.-Y.; Sun, Y.-W.; Tang, S.-Z.; Gu, M.-H. Development of gene-tagged markers for quantitative trait loci underlying rice yield components. Euphytica 2009, 169, 215–226. [Google Scholar] [CrossRef]
  76. Finn, R.; Griffiths-Jones, S.; Bateman, A. Identifying protein domains with the Pfam database. CurrProtoc Bioinform. 2003, 1, 2.5.1–2.5.19. [Google Scholar] [CrossRef]
  77. Marchler-Bauer, A.; Derbyshire, M.K.; Gonzales, N.R.; Lu, S.; Chitsaz, F.; Geer, L.Y.; Geer, R.C.; He, J.; Gwadz, M.; Hurwitz, D.I. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 2015, 43, D222–D226. [Google Scholar] [CrossRef]
  78. Letunic, I.; Doerks, T.; Bork, P. SMART 6: Recent updates and new developments. Nucleic Acids Res. 2009, 37, D229–D232. [Google Scholar] [CrossRef]
  79. Kozlowski, L.P. IPC–isoelectric point calculator. Biol. Direct 2016, 11, 55. [Google Scholar] [CrossRef]
  80. Nakai, K.; Horton, P. PSORT: A program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 1999, 24, 34–60. [Google Scholar] [CrossRef] [PubMed]
  81. Savojardo, C.; Martelli, P.L.; Fariselli, P.; Profiti, G.; Casadio, R. BUSCA: An integrative web server to predict subcellular localization of proteins. Nucleic Acids Res. 2018, 46, W459–W466. [Google Scholar] [CrossRef]
  82. Hu, B.; Jin, J.; Guo, A.Y.; Zhang, H.; Luo, J.; Gao, G. GSDS 2.0: An upgraded gene feature visualization server. Bioinformatics 2015, 31, 1296–1297. [Google Scholar] [CrossRef]
  83. Bailey, T.L.; Johnson, J.; Grant, C.E.; Noble, W.S. The MEME suite. Nucleic Acids Res. 2015, 43, W39–W49. [Google Scholar] [CrossRef]
  84. Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 2015, 10, 845–858. [Google Scholar] [CrossRef]
  85. Wolfe, D.; Dudek, S.; Ritchie, M.D.; Pendergrass, S.A. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 2013, 6, 18. [Google Scholar] [CrossRef]
  86. Wang, Y.; Tang, H.; DeBarry, J.D.; Tan, X.; Li, J.; Wang, X.; Lee, T.-h.; Jin, H.; Marler, B.; Guo, H. MCScanX: A toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012, 40, e49. [Google Scholar] [CrossRef]
  87. Chen, C.; Chen, H.; Zhang, Y.; Thomas, H.R.; Frank, M.H.; He, Y.; Xia, R. TBtools: An integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 2020, 13, 1194–1202. [Google Scholar] [CrossRef]
  88. Lescot, M.; Déhais, P.; Thijs, G.; Marchal, K.; Moreau, Y.; Van de Peer, Y.; Rouzé, P.; Rombauts, S. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 2002, 30, 325–327. [Google Scholar] [CrossRef]
  89. Du, Z.; Zhou, X.; Ling, Y.; Zhang, Z.; Su, Z. agriGO: A GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010, 38, W64–W70. [Google Scholar] [CrossRef]
  90. Metsalu, T.; Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015, 43, W566–W570. [Google Scholar] [CrossRef]
  91. Yau, S. Efficiency of alpha-lattice designs in international variety yield trials of barley and wheat. J. Agric. Sci. 1997, 128, 5–9. [Google Scholar] [CrossRef]
  92. Murray, M.; Thompson, W. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980, 8, 4321–4326. [Google Scholar] [CrossRef] [PubMed]
  93. Chidambaranathan, P.; Balasubramaniasai, C.; Behura, N.; Purty, M.; Samantaray, S.; Subudhi, H.; Ngangkham, U.; Devanna, B.; Katara, J.L.; Kumar, A. Effects of high temperature on spikelet sterility in rice (Oryza sativa L.): Association between molecular markers and allelic phenotypic effect in field condition. Genet. Resour. Crop Evol. 2021, 68, 1923–1935. [Google Scholar] [CrossRef]
  94. Chambers, J.M. Linear models. In Statistical Models in S; Routledge: Oxfordshire, UK, 2017; pp. 95–144. Available online: https://www.routledge.com/Statistical-Models-in-S/Chambers-Hastie/p/book/9780412830402 (accessed on 13 July 2023).
  95. Babu, B.K.; Meena, V.; Agarwal, V.; Agrawal, P. Population structure and genetic diversity analysis of Indian and exotic rice (Oryza sativa L.) accessions using SSR markers. Mol. Biol. Rep. 2014, 41, 4329–4339. [Google Scholar] [CrossRef]
  96. Singh, N.; Choudhury, D.R.; Singh, A.K.; Kumar, S.; Srinivasan, K.; Tyagi, R.; Singh, N.; Singh, R. Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PLoS ONE 2013, 8, e84136. [Google Scholar] [CrossRef]
  97. Nachimuthu, V.V.; Muthurajan, R.; Duraialaguraja, S.; Sivakami, R.; Pandian, B.A.; Ponniah, G.; Gunasekaran, K.; Swaminathan, M.; KK, S.; Sabariappan, R. Analysis of population structure and genetic diversity in rice germplasm using SSR markers: An initiative towards association mapping of agronomic traits in Oryza sativa. Rice 2015, 8, 30. [Google Scholar] [CrossRef]
Plants 12 03035 g001
Figure 1. The phylogenetic tree of CYP proteins was established for A. thaliana, O. sativa, and S. lycopersicum using the neighbor-joining method in MEGA 11. To gauge the tree’s reliability, 1000 bootstrap replicates were employed. OsCYP71 genes could be divided into 12 subfamilies (I–XII).
Figure 1. The phylogenetic tree of CYP proteins was established for A. thaliana, O. sativa, and S. lycopersicum using the neighbor-joining method in MEGA 11. To gauge the tree’s reliability, 1000 bootstrap replicates were employed. OsCYP71 genes could be divided into 12 subfamilies (I–XII).
Plants 12 03035 g001
Plants 12 03035 g002
Figure 2. Chromosome distribution of identified OsCYP71 genes. (A) Schematic illustrations of the chromosomal distribution of CYP genes on the twelve chromosomes of rice with the gene positions on each chromosome represented by a line on the right side. (B) Dispersal of CYP71 genes across eleven chromosomes of rice.
Figure 2. Chromosome distribution of identified OsCYP71 genes. (A) Schematic illustrations of the chromosomal distribution of CYP genes on the twelve chromosomes of rice with the gene positions on each chromosome represented by a line on the right side. (B) Dispersal of CYP71 genes across eleven chromosomes of rice.
Plants 12 03035 g002
Plants 12 03035 g003
Figure 3. Chromosomal distribution and duplicated CYP gene pairs in rice. Duplicated CYP gene pairs are connected with distinct colors of lines. The figure was constructed using TB tools with numbers 1–12 denoting the rice chromosomes.
Figure 3. Chromosomal distribution and duplicated CYP gene pairs in rice. Duplicated CYP gene pairs are connected with distinct colors of lines. The figure was constructed using TB tools with numbers 1–12 denoting the rice chromosomes.
Plants 12 03035 g003
Plants 12 03035 g004
Figure 4. The intron-exon structure of the OsCYP71 genes. The yellow boxes signify exons while black lines denote introns. The lengths of the boxes and lines are scaled based on gene length.
Figure 4. The intron-exon structure of the OsCYP71 genes. The yellow boxes signify exons while black lines denote introns. The lengths of the boxes and lines are scaled based on gene length.
Plants 12 03035 g004
Plants 12 03035 g005aPlants 12 03035 g005b
Figure 5. The conserved motifs in OsCYP71 genes. The conserved motifs were elucidated by the MEME database. (A) The distinct colored boxes represent different conserved motifs with variable sizes and sequences. (B) Sequence logo of the conserved motif of OsCYP71.
Figure 5. The conserved motifs in OsCYP71 genes. The conserved motifs were elucidated by the MEME database. (A) The distinct colored boxes represent different conserved motifs with variable sizes and sequences. (B) Sequence logo of the conserved motif of OsCYP71.
Plants 12 03035 g005aPlants 12 03035 g005b
Plants 12 03035 g006aPlants 12 03035 g006b
Figure 6. Identification of cis-regulatory elements in the 1500 bp promoter region of the OsCYP71 gene family. (A) Phytohormone-responsive elements, light-responsive elements, growth- and development-related elements, stress-responsive elements and other elements with unknown functions are shown by distinct colors. (B) The numbers of different CAREs found in OsCYP71 gene family members.
Figure 6. Identification of cis-regulatory elements in the 1500 bp promoter region of the OsCYP71 gene family. (A) Phytohormone-responsive elements, light-responsive elements, growth- and development-related elements, stress-responsive elements and other elements with unknown functions are shown by distinct colors. (B) The numbers of different CAREs found in OsCYP71 gene family members.
Plants 12 03035 g006aPlants 12 03035 g006b
Plants 12 03035 g007aPlants 12 03035 g007bPlants 12 03035 g007c
Figure 7. Expression profiles of OsCYP71 genes in different tissues and hormone treatments. (A) The expression profile in various tissues and developmental stages. (B) The expression profile in shoots under different hormone treatments. (C) The expression profile in roots under different hormone treatments.
Figure 7. Expression profiles of OsCYP71 genes in different tissues and hormone treatments. (A) The expression profile in various tissues and developmental stages. (B) The expression profile in shoots under different hormone treatments. (C) The expression profile in roots under different hormone treatments.
Plants 12 03035 g007aPlants 12 03035 g007bPlants 12 03035 g007c
Plants 12 03035 g008
Figure 8. Phylogenetic analysis of rice varieties through the unweighted pair group method with arithmetic averaging (UPGMA) model using in/dels allelic variants of OsCYP71P6. Cluster A and Cluster B indicate major clusters formed using distance-based diversity analysis. V—Varieties. The names of the varieties are given in Table S7.
Figure 8. Phylogenetic analysis of rice varieties through the unweighted pair group method with arithmetic averaging (UPGMA) model using in/dels allelic variants of OsCYP71P6. Cluster A and Cluster B indicate major clusters formed using distance-based diversity analysis. V—Varieties. The names of the varieties are given in Table S7.
Plants 12 03035 g008
Plants 12 03035 g009
Figure 9. Model-based diversity analysis and analysis of molecular variance (AMOVA) for sub-populations. (A) Sub-structure analysis of rice varieties using the allelic variants of OsCYP71P6. (B) Delta K population structure plot for the rice varieties produced through the STRUCTURE harvester program. (C) Pie chart of the percent distribution of the analysis of molecular variance (AMOVA) for the OsCYP71P6 alleles in rice varieties.
Figure 9. Model-based diversity analysis and analysis of molecular variance (AMOVA) for sub-populations. (A) Sub-structure analysis of rice varieties using the allelic variants of OsCYP71P6. (B) Delta K population structure plot for the rice varieties produced through the STRUCTURE harvester program. (C) Pie chart of the percent distribution of the analysis of molecular variance (AMOVA) for the OsCYP71P6 alleles in rice varieties.
Plants 12 03035 g009
Plants 12 03035 g010
Figure 10. Yield differences between the four haplotypes of OsCYP71P6 allelic variants. Hap 1-4 indicates the four different haplotypes of OsCYP71P6-1 and OsCYP71P6-4 alleles. No. of varieties present in each haplotype is represented by a line graph. Standard error was used to plot the error bar in the bar chart. The number indicates the mean yield and no. of varieties for the haplotypes. Statistical difference in the mean of the single-plant yield between the haplotypes was analyzed using the Z test for mean difference. p = 0.005 represents the statistically significant mean difference value calculated using the Z Test at a 1% level of significance. Hap1: OsCYP71P6-1320bp, OsCYP71P6-4380bp, Hap2: OsCYP71P6-1350bp, OsCYP71P6-4380bp, Hap3: OsCYP71P6-1320bp, OsCYP71P6-400bp, Hap4: OsCYP71P6-1350bp, OsCYP71P6-400bp, bp-base pair (amplicon size).
Figure 10. Yield differences between the four haplotypes of OsCYP71P6 allelic variants. Hap 1-4 indicates the four different haplotypes of OsCYP71P6-1 and OsCYP71P6-4 alleles. No. of varieties present in each haplotype is represented by a line graph. Standard error was used to plot the error bar in the bar chart. The number indicates the mean yield and no. of varieties for the haplotypes. Statistical difference in the mean of the single-plant yield between the haplotypes was analyzed using the Z test for mean difference. p = 0.005 represents the statistically significant mean difference value calculated using the Z Test at a 1% level of significance. Hap1: OsCYP71P6-1320bp, OsCYP71P6-4380bp, Hap2: OsCYP71P6-1350bp, OsCYP71P6-4380bp, Hap3: OsCYP71P6-1320bp, OsCYP71P6-400bp, Hap4: OsCYP71P6-1350bp, OsCYP71P6-400bp, bp-base pair (amplicon size).
Plants 12 03035 g010
Table 1. The identified putative cytochrome P450 (OsCYP71) genes in rice and their biophysical characteristics.
Table 1. The identified putative cytochrome P450 (OsCYP71) genes in rice and their biophysical characteristics.
Proposed Gene NameGene IDChromosomeGenomic LocationOrientationCDS Length (bp)Protein Length (aa)Molecular Weight (KDa)Isoelectric Point (pI)GRAVYPredicted Subcellular Localization
OsCYP71K1BGIOSGA00161011:17410528-17412154Reverse155751857.417.730.0144endomembrane system
OsCYP71C1BGIOSGA00371511:22500639-22503588Forward1413470535.85−0.172plasma membrane
OsCYP71U1BGIOSGA00427411:32296506-32298186Forward158452758.278.09−0.092endomembrane system
OsCYP71AA1BGIOSGA00520911:46261115-46263199Forward157252358.128.310.007endomembrane system
OsCYP71AA2BGIOSGA00521011:46266756-46268515Forward160553459.458.35−0.029endomembrane system
OsCYP71T1BGIOSGA00304911:7516842-7519131Forward168956260.515.640.051endomembrane system
OsCYP71T2BGIOSGA00305011:7524389-7527482Forward169256360.536.620.019endomembrane system
OsCYP71T3BGIOSGA00305411:7558245-7560317Forward161453759.187.42−0.018endomembrane system
OsCYP71U4BGIOSGA00796222:11083073-11087397Forward150350054.156.47−0.027endomembrane system
OsCYP71AB6BGIOSGA00825522:18910371-18913246Forward151850556.158.88−0.098endomembrane system
OsCYP71W1BGIOSGA00826122:19021940-19025270Forward161453760.18.70−0.063endomembrane system
OsCYP71U5BGIOSGA00826522:19083433-19088060Forward159052959.118.17−0.146endomembrane system
OsCYP71U6BGIOSGA00826622:19089067-19092362Forward157852558.868.43−0.168endomembrane system
OsCYP71Z4BGIOSGA00633922:20885281-20889418Reverse154251356.97.98−0.02plasma membrane
OsCYP71T4BGIOSGA00846322:23403404-23404909Forward150650155.318.000.032endomembrane system
OsCYP71Z5BGIOSGA00846822:23502957-23505833Forward156652157.348.22−0.004endomembrane system
OsCYP71Z6BGIOSGA00621522:23525585-23528377Reverse155751857.467.51−0.028endomembrane system
OsCYP71T5BGIOSGA00621022:23642185-23643714Reverse153050956.077.03−0.009endomembrane system
OsCYP71X1BGIOSGA00700222:5495789-5497484Reverse156051957.577.63−0.07endomembrane system
OsCYP71K2BGIOSGA00768322:5517065-5518619Forward96632136.355.45−0.089endomembrane system
OsCYP71X2BGIOSGA00768622:5560619-5562432Forward156652157.656.82−0.031endomembrane system
OsCYP71X3BGIOSGA00768822:5569750-5571671Forward154851558.057.74−0.131endomembrane system
OsCYP71X4BGIOSGA00769122:5604868-5606669Forward156652157.496.78−0.042endomembrane system
OsCYP71K3BGIOSGA00769422:5642839-5644448Forward153050956.328.600.057endomembrane system
OsCYP71AC1BGIOSGA00769522:5644996-5646510Forward142247352.178.18−0.055endomembrane system
OsCYP71K4BGIOSGA00769622:5647245-5648907Forward157252357.787.54−0.052endomembrane system
OsCYP71V1BGIOSGA00779122:7497274-7499270Forward153651156.68.060.006plasma membrane
OsCYP71V2BGIOSGA00779222:7500786-7503251Forward152750856.497.970.057endomembrane system
OsCYP71P4BGIOSGA01150833:2163933-2165882Reverse164454759.26.950.047endomembrane system
OsCYP71T6BGIOSGA01044733:21911994-21913514Reverse152150655.756.450.009endomembrane system
OsCYP71E2BGIOSGA01299233:23090126-23097523Forward155451757.427.58−0.409plasma membrane
OsCYP71W2BGIOSGA01305733:25210900-25212580Forward153651157.57.69−0.096endomembrane system
OsCYP71W3BGIOSGA01305933:25254727-25257057Forward161453760.767.42−0.094endomembrane system
OsCYP71W4BGIOSGA01306333:25338238-25340280Forward154251357.487.67−0.063endomembrane system
OsCYP71AB7BGIOSGA01014033:29073720-29075333Reverse161453757.397.980.06endomembrane system
OsCYP71V3BGIOSGA01353633:34645395-34647054Forward150650155.229.300.034endomembrane system
OsCYP71U7BGIOSGA01360233:35634216-35637043Forward142547452.317.62−0.047endomembrane system
OsCYP71U8BGIOSGA01360433:35641266-35643533Forward154251356.617.74−0.012endomembrane system
OsCYP71U9BGIOSGA01360533:35649465-35651890Forward158452758.397.46−0.07endomembrane system
OsCYP71U10BGIOSGA01360633:35654380-35656113Forward155751857.898.64−0.033endomembrane system
OsCYP71E3BGIOSGA01394833:40194199-40200533Forward158452758.429.44−0.028endomembrane system
OsCYP71AB8BGIOSGA01111233:8385202-8386836Reverse150650155.398.28−0.033endomembrane system
OsCYP71AB9BGIOSGA01111133:8394147-8396376Reverse150350056.228.12−0.15endomembrane system
OsCYP71AB10BGIOSGA01158633:954097-955621Reverse144648152.387.520.105endomembrane system
OsCYP71Z7BGIOSGA01614644:13300800-13303842Forward153651156.748.240.0001endomembrane system
OsCYP71S2BGIOSGA01486744:22441383-22445458Reverse230176684.898.42−0.324endomembrane system
OsCYP71S3BGIOSGA01486644:22450395-22452270Reverse153651155.888.52−0.074endomembrane system
OsCYP71Z8BGIOSGA01550444:6445565-6447178Reverse152450756.656.89−0.014endomembrane system
OsCYP71Z9BGIOSGA01598144:6553171-6555010Forward150650155.766.040.011endomembrane system
OsCYP71S4BGIOSGA01574344:89164-90761Reverse150049955.327.46−0.05endomembrane system
OsCYP71AF2BGIOSGA01969855:18144104-18145713Forward153651155.49.54−0.096endomembrane system
OsCYP71ADBGIOSGA01802655:22038254-22039816Reverse156352057.16.35−0.087endomembrane system
OsCYP71P5BGIOSGA02009855:25741450-25745152Forward153951258.368.73−0.237endomembrane system
OsCYP71R1BGIOSGA02018555:27004831-27006483Forward156952256.666.64−0.083endomembrane system
OsCYP71P6BGIOSGA01852355:8791390-8793045Reverse156051957.327.62−0.067mitochondrial membrane
OsCYP71C2BGIOSGA02280966:13456419-13457960Forward141647153.448.780.002endomembrane system
OsCYP71T7BGIOSGA02118966:18739993-18741510Reverse151850555.677.520.049endomembrane system
OsCYP71S1BGIOSGA02212266:190381-192067Forward155751856.418.180.007endomembrane system
OsCYP71AB11BGIOSGA02089066:25633293-25634951Reverse158452757.958.12−0.085endomembrane system
OsCYP71K5BGIOSGA02334066:27270045-27274299Forward156051957.078.65−0.042endomembrane system
OsCYP71Y1BGIOSGA02334166:27281079-27282759Forward153951255.467.930.089endomembrane system
OsCYP71Y2BGIOSGA02334566:27304346-27305982Forward156952257.417.70−0.039endomembrane system
OsCYP71Y3BGIOSGA02334666:27308296-27309910Forward154551456.397.86−0.008endomembrane system
OsCYP71K6BGIOSGA02335066:27362563-27367339Forward150350056.99.67−0.053endomembrane system
OsCYP71AC2BGIOSGA02335166:27374980-27377638Forward163254360.637.55−0.133endomembrane system
OsCYP71AF1BGIOSGA02080566:27378645-27380259Reverse151550455.246.38−0.001endomembrane system
OsCYP71X5BGIOSGA02070466:29562437-29565945Reverse164154660.568.19−0.125endomembrane system
OsCYP71Q1BGIOSGA02445677:11074316-11076970Reverse111337042.155.61−0.053endomembrane system
OsCYP71AB12BGIOSGA02793388:1799581-1805006Forward34981165130.47.42−0.086mitochondrial membrane
OsCYP71W5BGIOSGA02688088:23757559-23759121Reverse156352058.357.73−0.038endomembrane system
OsCYP71T8BGIOSGA02880988:24254355-24255857Forward150350055.998.220.037endomembrane system
OsCYP71T9BGIOSGA02671188:26774318-26775838Reverse152150656.357.19−0.012endomembrane system
OsCYP71U11BGIOSGA02653088:29302265-29303901Reverse155451756.336.26−0.008endomembrane system
OsCYP71C3BGIOSGA02779388:327857-329461Reverse160553459.946.63−0.003plasma membrane
OsCYP71C4BGIOSGA02779188:335792-337318Reverse152750857.556.70−0.012endomembrane system
OsCYP71C5BGIOSGA02779088:341090-342922Reverse165054962.016.04−0.192endomembrane system
OsCYP71C6BGIOSGA02784088:347325-348858Forward138946252.157.86−0.084endomembrane system
OsCYP71C7BGIOSGA02784188:358473-360517Forward157552459.228.46−0.0001endomembrane system
OsCYP71AB13BGIOSGA02743788:8239548-8245451Reverse157552458.189.90−0.082endomembrane system
OsCYP71W6BGIOSGA03084199:14907658-14909929Forward154551458.118.71−0.125endomembrane system
OsCYP71W7BGIOSGA03084299:14912617-14916216Forward155751858.967.81−0.107endomembrane system
OsCYP71W8BGIOSGA02968299:14918713-14922927Reverse156952259.658.50−0.208endomembrane system
OsCYP71E4BGIOSGA02966499:15258631-15261118Reverse153351055.497.010.003endomembrane system
OsCYP71T10BGIOSGA02966399:15266334-15268285Reverse151850554.627.390.031endomembrane system
OsCYP71T11BGIOSGA03113499:19550851-19553047Forward144648152.456.58−0.002endomembrane system
OsCYP71AKBGIOSGA03113599:19556511-19558129Forward152150654.638.97−0.012endomembrane system
OsCYP71C8BGIOSGA03009799:5147576-5149433Reverse153050956.538.78−0.032endomembrane system
OsCYP71Z1BGIOSGA0318441010:14240993-14243733Reverse157252358.47.21−0.019endomembrane system
OsCYP71Z2BGIOSGA0318431010:14247493-14251099Reverse157552458.47.80−0.098endomembrane system
OsCYP71Z3BGIOSGA0318421010:14257972-14262017Reverse156051957.98.56−0.119endomembrane system
OsCYP71AB1BGIOSGA0325801010:3806982-3812472Forward150650156.158.03−0.045endomembrane system
OsCYP71AB2BGIOSGA0325831010:3912905-3915694Forward148549455.268.18−0.047endomembrane system
OsCYP71AB3BGIOSGA0325841010:3947165-3951250Forward149749855.479.16−0.038endomembrane system
OsCYP71AB4BGIOSGA0325991010:4268768-4270367Forward151250357.96.60−0.168endomembrane system
OsCYP71AB5BGIOSGA0326341010:5163945-5165932Forward150950255.789.240.024endomembrane system
OsCYP71P1BGIOSGA0326531010:5794451-5798343Forward154251357.96.92−0.155endomembrane system
OsCYP71P2BGIOSGA0326801010:6875415-6884974Forward160853558.496.560.02endomembrane system
OsCYP71P3BGIOSGA0326831010:7021038-7022711Forward158152657.527.67−0.072endomembrane system
OsCYP71E1BGIOSGA0361061212:16043912-16046285Reverse156952258.428.83−0.166endomembrane system
OsCYP71U2BGIOSGA0359351212:19695567-19697582Reverse155751855.446.750.149endomembrane system
OsCYP71U3BGIOSGA0359341212:19701585-19703297Reverse171357060.727.150.197endomembrane system
OsCYP71V4BGIOSGA037944ScaffoldAAAA02035682.1:5479-7227Forward154251357.37.36−0.012endomembrane system
OsCYP71W9BGIOSGA038041ScaffoldAAAA02036020.1:14706-17551Forward153050957.078.21−0.305endomembrane system
OsCYP71U12BGIOSGA038175ScaffoldAAAA02036741.1:10396-12075Reverse156652157.98.24−0.071endomembrane system
OsCYP71AB14BGIOSGA038318ScaffoldAAAA02037602.1:3877-5590Forward150950256.249.26−0.25endomembrane system
ID: identity; bp: base pair; aa: amino acids; pI: isoelectric point; MW: molecular weight; GRAVY: grand average of hydropathy; KDa: Kilo dalton.
Table 2. Analysis of gene diversity for the two OsCYP71P6 in/del markers across various rice varieties.
Table 2. Analysis of gene diversity for the two OsCYP71P6 in/del markers across various rice varieties.
MarkerMajor Allele FrequencyNo. of VarietiesAllele NoGene DiversityHeterozygosityPIC
CYP71P6-10.86261312.00000.237000.2090
CYP71P6-40.81681312.00000.299300.2545
Mean0.83971312.00000.268200.2317
Table 3. Analysis of molecular variance (AMOVA) for two-subpopulations and admixtures of the OsCYP71P6 in/dels variants in rice varieties.
Table 3. Analysis of molecular variance (AMOVA) for two-subpopulations and admixtures of the OsCYP71P6 in/dels variants in rice varieties.
Source of VariationdfSSMSEst. Var.Percent Variation (%)
Among Populations139.25639.2560.49881%
Among Individual varieties12931.0030.2400.12019%
Within Individual varieties1310.0000.0000.0000%
Total26170.260 0.618100%
Table 4. Descriptive statistics analysis of in/dels variants of the OsCYP71P6 gene for yield-related traits in rice varieties.
Table 4. Descriptive statistics analysis of in/dels variants of the OsCYP71P6 gene for yield-related traits in rice varieties.
TraitMeanMedianModeKurtosisSkewness
PrimerOsCYP71P6-1OsCYP71 P6-4OsCYP71 P6-1OsCYP71 P6-4OsCYP71 P6-1OsCYP71 P6-4OsCYP71 P6-1OsCYP71 P6-4OsCYP71 P6-1OsCYP71 P6-4
OsCYP71P6
Amplicon length (bp)320350380400320350380400320350380400320350380400320350380400
No. of tillers (Nos.)10.2911.98 a10.3211.411010.331010.661210.331216.66−0.082.191.62−0.680.431.20.820.25
Panicle length (cm)26.1526.2425.8927.36 b26.225.9625.927.2126.46_26.46_0.78−0.910.65−0.08−0.320.33−0.270.41
Single-plant yield (g)31.2137.9 a30.7138.48 b30.0634.729.836.2633.65_33.65_0.811.112.64−0.830.671.121.030.28
No. of spikelets (Nos.)168.26171.81163.99189.93 b165.66176.83163.33190.16170.33_170.3165.660.29−0.020.480.450.27−0.180.270.12
Unfilled grain (Nos.)33.9529.230.0847.65 b3126.8327.664427.66_27.66447.581.270.015.431.851.080.681.81
Filled grain (Nos.)134.3142.61133.91142.27134.66150134.66150.5133_1331560.5−0.241.02−0.070.39−0.370.48−0.34
Panicle weight (g)3.323.523.293.613.233.553.233.673.06_3.182.986.04−0.037.090.271.370.291.520.1
100 seed weight (g)2.32.272.272.42.352.262.322.392.382.22.38_1.281.221.331.41−0.810.15−0.73−0.78
_ Indicates not determined, bold values indicate statistically significant mean difference between the different alleles of CYP71P6. a—Significance at 5% level of significance, b—significance at 1% level of significance. The Z test was used for the mean difference analysis.
Table 5. The population and haplotype mean difference analysis for yield-related traits in OsCYP71P6 in/dels rice variants.
Table 5. The population and haplotype mean difference analysis for yield-related traits in OsCYP71P6 in/dels rice variants.
Primer NameTraits aMean ± SD, AL320 bMean ± SD, AL350 bp Value c
OsCYP71P6-1
(In varieties)		NT10.29 ± 3.0911.98 ± 4.090.04, *
SPY31.21 ± 11.4237.90 ± 15.380.03, *
OsCYP71P6-4
(In varieties)		TraitsMean ± SD, AL380 bMean ± SD, AL400 bp value
SPY30.71 ± 11.3838.48 ± 13.870.005, **
NS163.99 ± 54.16189.93 ± 43.860.004, **
PW3.29 ± 0.953.61 ± 0.840.05, *
PL25.89 ± 2.8127.36 ± 2.260.002, **
UG30.08 ± 15.8647.65 ± 28.660.001, **
OsCYP71P6-1 and OsCYP71P6-4
(In two subpopulations)		TraitsMean ± SD, Sub-Pop1Mean ± SD, Sub-Pop2p value
SPY37.06 ± 15.3331.42 ± 10.480.03, *
FG150.93 ± 28.71135.32 ± 43.630.02, *
OsCYP71P6-1 and OsCYP71P6-4
(In two subpopulations and admixtures)		TraitMean ± SD, Sub-Pop1Mean ± SD, Sub-Pop1Mean ± SD, Admixp value
SPY37.06 ± 15.3331.42 ± 10.4829.74 ± 11.370.03, *
NS180.12 ± 35.8172.97 ± 49.09154.85 ± 42.230.04, *
FG150.93 ± 28.71135.32 ± 43.63124.92 ± 35.770.02, *
OsCYP71P6-1 and OsCYP71P6-4
(In four haplotypes)		TraitsMean ± SD, Hap1Mean ± SD, Hap2Mean ± SD, Hap3Mean ± SD, Hap4p value
SPY29.81 ± 10.2437.71 ± 14.4136.64 ± 16.4342.32 ± 11.730.005, **
a—NT-No. of tillers, SPY—single-plant yield, NS—no. of spikelets, PW—panicle weight, PL—panicle length, UG—no. of unfilled grains, FG—no. of filled grains, b—AL-amplicon length of the in/del primer, c—Z test p value for mean difference. Double asterisk indicates 1% level of significance and single asterisk indicates significance at 5% level.
Table 6. Linear regression analysis for the association of genetic variants of OsCYP71P6 in the 3K database with spikelet fertility score in rice.
Table 6. Linear regression analysis for the association of genetic variants of OsCYP71P6 in the 3K database with spikelet fertility score in rice.
Sl.NoSNP Position aGene PositionAmino Acid Substitutionp Value d
1Chr12:9581604First Exon (98C>T) bSer33Leu c0.005767 **
2Chr12: 9582455Promoter-0.003087 **
3Chr12: 9582489Promoter-0.019201 *
4Chr12: 9582557Promoter-0.047372 *
5Chr12: 9582591Promoter-0.004385 **
6Chr12: 9582869Promoter-0.001087 **
7Chr12: 9583776Promoter-0.020460 *
8Chr12: 9583083Promoter-0.027545 *
9Chr12: 9582921Promoter-0.003404 **
a—SNP position indicates the nucleotide position significantly associated with the spikelet fertility score, b—98C>T indicates the 98th nucleotide substitution in the coding sequence from the start codon of CYP71A1, c—serine is substituted by leucine amino acid in the 33rd position of the protein, d—double asterisk indicates significance at 1% level of significance and single asterisk indicates significance at 5% level.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sahoo, B.; Nayak, I.; Parameswaran, C.; Kesawat, M.S.; Sahoo, K.K.; Subudhi, H.N.; Balasubramaniasai, C.; Prabhukarthikeyan, S.R.; Katara, J.L.; Dash, S.K.; et al. A Comprehensive Genome-Wide Investigation of the Cytochrome 71 (OsCYP71) Gene Family: Revealing the Impact of Promoter and Gene Variants (Ser33Leu) of OsCYP71P6 on Yield-Related Traits in Indica Rice (Oryza sativa L.). Plants 2023, 12, 3035. https://doi.org/10.3390/plants12173035

AMA Style

Sahoo B, Nayak I, Parameswaran C, Kesawat MS, Sahoo KK, Subudhi HN, Balasubramaniasai C, Prabhukarthikeyan SR, Katara JL, Dash SK, et al. A Comprehensive Genome-Wide Investigation of the Cytochrome 71 (OsCYP71) Gene Family: Revealing the Impact of Promoter and Gene Variants (Ser33Leu) of OsCYP71P6 on Yield-Related Traits in Indica Rice (Oryza sativa L.). Plants. 2023; 12(17):3035. https://doi.org/10.3390/plants12173035

Chicago/Turabian Style

Sahoo, Bijayalaxmi, Itishree Nayak, C. Parameswaran, Mahipal Singh Kesawat, Khirod Kumar Sahoo, H. N. Subudhi, Cayalvizhi Balasubramaniasai, S. R. Prabhukarthikeyan, Jawahar Lal Katara, Sushanta Kumar Dash, and et al. 2023. "A Comprehensive Genome-Wide Investigation of the Cytochrome 71 (OsCYP71) Gene Family: Revealing the Impact of Promoter and Gene Variants (Ser33Leu) of OsCYP71P6 on Yield-Related Traits in Indica Rice (Oryza sativa L.)" Plants 12, no. 17: 3035. https://doi.org/10.3390/plants12173035

APA Style

Sahoo, B., Nayak, I., Parameswaran, C., Kesawat, M. S., Sahoo, K. K., Subudhi, H. N., Balasubramaniasai, C., Prabhukarthikeyan, S. R., Katara, J. L., Dash, S. K., Chung, S. -M., Siddiqui, M. H., Alamri, S., & Samantaray, S. (2023). A Comprehensive Genome-Wide Investigation of the Cytochrome 71 (OsCYP71) Gene Family: Revealing the Impact of Promoter and Gene Variants (Ser33Leu) of OsCYP71P6 on Yield-Related Traits in Indica Rice (Oryza sativa L.). Plants, 12(17), 3035. https://doi.org/10.3390/plants12173035

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop