A multi-layered regulatory model uncovers the central role of OsPRR37 in coordinating multiple agronomic traits

A multi-layered regulatory model uncovers the central role of OsPRR37 in coordinating multiple agronomic traits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A multi-layered regulatory model uncovers the central role of OsPRR37 in coordinating multiple agronomic traits Chuan Liu, Lu Liu, Yilong Liang, Yinghong Li, Ying Liu, Jin Dai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7366051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2025 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Background: The plant circadian clock is crucial for regulating developmental and metabolic processes, enabling crops to adapt to environmental changes and maintain high productivity. In rice, the clock gene OsPRR37 plays a pivotal role in photoperiod sensitivity and the regulation of yield-related traits. However, the complete regulatory network of OsPRR37 remains largely unexplored. Results: This study utilized an integrated multi-omics approach, combining transcriptome profiling, DNA affinity purification sequencing (DAP-seq), and protein–protein interaction (PPI) mapping to construct a multi-layered regulatory model of OsPRR37 . CRISPR/Cas9 knockout lines showed significant changes in flowering time, plant height, panicle architecture, and spikelet number. Transcriptome analysis associated OsPRR37 with pathways related to photosynthesis, carbohydrate metabolism, and stress responses. Comparative analysis of knockout and overexpression datasets identified 454 candidate target genes exhibiting inverse expression patterns, including regulators of flowering and chlorophyll biosynthesis. DAP-seq revealed 1,679 high-confidence DNA-binding sites, with nine genes identified as direct targets, six of which contained conserved motifs associated with cytokinin signaling, inflorescence architecture, and meristem determinacy. PPI mapping through a yeast two-hybrid screen identified 26 interacting proteins, including OsGlyRS3 and OsSnRK1A, which are involved in flowering, sugar signaling, chloroplast development, and hormone metabolism. Structural modeling suggested that OsGlyRS3 may stabilize OsPRR37 protein complexes, while OsSnRK1A could modulate its DNA-binding capacity under sugar-deficient conditions. Conclusions: The findings establish OsPRR37 as a central regulatory hub that coordinates flowering, energy metabolism, chloroplast function, and stress adaptation through a hierarchical network comprising a Modulatory Layer of protein interactors, a Direct Target Layer of DNA-bound genes, an Indirect Coherent Layer of transcriptional cascades, and a Diffuse Response Layer encompassing broad metabolic outputs. This model provides a comprehensive framework for understanding how OsPRR37 integrates circadian signals to control multiple agronomic traits and offers valuable targets for breeding climate-resilient, high-yielding rice varieties. Oryza sativa L. Circadian clock Multi-omics Gene expression regulation Protein-protein interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Plant circadian clock genes play a central role in enabling plants to adapt to fluctuating environmental conditions and sustain high productivity. Leveraging natural genetic variation in these genes and refining their regulatory mechanisms through targeted molecular strategies offers a promising approach to developing climate-resilient crops with enhanced tolerance to environmental stresses [ 1 ]. The plant circadian clock is organized as a network of interlocked transcriptional–translational feedback loops composed of transcriptional activators and repressors that regulate both the core oscillator and its output pathways [ 2 ]. While the clock has long been considered to be dominated by repressive interactions [ 3 , 4 ], recent studies have uncovered transcriptional activators and even dual-function transcription factors, revealing greater regulatory complexity than previously understood [ 5 ]. Pseudo-response regulators (PRRs), as members of the two-component system, are central components of the circadian clock that integrate environmental signals and coordinate diverse biological processes, including mitochondrial function, metabolism, and agronomic traits such as flowering time; however, how these PRRs transduce signals to downstream target genes and pathways remains unclear [ 6 – 8 ]. In rice, OsPRR37 (also known as Ghd7.1 , DTH7 or Hd2 ), a key locus underlying photoperiod sensitivity, has emerged as a major regulator linking circadian rhythms with multiple yield-related agronomic traits [ 9 – 14 ]. OsPRR37 modulates the rhythmic expression of over half of the rice transcriptome and represses key flowering genes such as Ehd1 in an expression level-dependent manner [ 15 ]. Beyond transcriptional regulation, OsPRR37 also influences epigenetic modifications, including CG and CHG methylation changes in output genes, impacting starch metabolism, plant growth, and flowering time [ 16 ]. Recent findings further revealed that OsPRR37 / Ghd7.1 enhances rice eating quality by reducing grain protein content through repression of OsAAP6 [ 17 ]. Despite these advances, the downstream targets of OsPRR37 and its integrated regulatory network remain largely unresolved. Studies of Arabidopsis core clock components highlight the complexity of circadian regulatory hierarchies. Genome-wide DNA-binding and expression analyses have shown that PRR5 and PRR7 directly interact with PHYTOCHROME-INTERACTING FACTOR (PIF) transcription factors at target promoters, repressing their transcriptional activation and subsequent shade-avoidance responses [ 18 ]. More recently, quantitative studies of the core clock protein CCA1 revealed that, despite its high abundance (~ 100,000 molecules per cell), its DNA binding was lower than expected, suggesting the presence of additional regulatory layers such as post-translational modifications, protein–protein interactions, and chromatin-level constraints [ 19 ]. Yeast two-hybrid screening and multi-omics approaches, including RNA-seq and DNA affinity purification sequencing (DAP-seq), have proven highly effective in uncovering these layers by linking protein–protein interactions and circadian-regulated transcription factor binding to gene expression rhythms [ 20 – 23 ]. Applying these approaches to OsPRR37 will provide valuable insight into its interaction networks, direct targets, and output pathways, ultimately enabling the construction of a comprehensive circadian clock–centered regulatory model. Building on these insights, we propose a multi-layered regulatory framework to elucidate how OsPRR37 coordinates downstream gene expression. At the Modulatory Layer (Level 1), OsPRR37 forms complexes with specific protein partners—identified through yeast two-hybrid screening—that modulate its transcriptional activity, DNA-binding specificity, and context-dependent regulatory functions. The Direct Target Layer (Level 2) consists of genes directly bound and transcriptionally regulated by OsPRR37, as identified through DAP-seq and RNA-seq. The Indirect Coherent Layer (Level 3) encompasses genes not directly bound by OsPRR37 but consistently up- or down-regulated in both overexpression and knockout backgrounds, reflecting regulation through intermediary factors or transcriptional cascades. Finally, the Diffuse Response Layer (Level 4) includes genes with variable or non-directional expression changes following OsPRR37 perturbation, likely representing broader downstream effects or system-level feedback. Elucidating this multi-layered regulatory framework by integrating protein–protein interaction, transcriptional, and functional genomics data will enhance our understanding of how circadian regulatory networks coordinate multiple agronomic traits. Materials and Methods Plant Materials and Growth Conditions The nearly isogenic line of OsPRR37 (NIL37) was derived from the elite rice variety Guangluai 4 (GL) and contains the functional allele of OsPRR37 from the elite variety Teqing. Knock-out lines (KO42 and KO27) were generated using CRISPR/Cas9 technology to target the coding sequence of OsPRR37 in the NIL37 line. Diurnal transcriptome data previously collected from GL and the OsPRR37 overexpression line OE5 (where OsPRR37 was overexpressed in GL) were utilized [ 15 ]. Rice growth phenotypes were observed under natural long-day conditions in Chongqing, China. For DAP-seq analysis, NIL37 seeds were grown in a controlled growth chamber (PRX-380B, Shanghai Guning Instrument Co., Ltd.) for 15 days post-germination, under a 28°C environment with a 14-hour light/10-hour dark cycle (light from 6:00 AM to 8:00 PM). For RNA-seq analysis, NIL37 and KO42 seeds were grown for 15 days under natural long-day conditions in Chongqing, China. The topmost expanded leaves were collected at 8:00 AM, immediately frozen in liquid nitrogen, and stored at -80°C for subsequent DNA and RNA extraction. Three and two biological replicates were prepared for RNA-seq and DAP-seq analyses, respectively. RNA Library Generation and Sequencing Total RNA was extracted from rice leaves using TRIzol Reagent (Invitrogen, CA, USA) for RNA sequencing. The RNA quality was evaluated using a NanoPhotometer® (IMPLEN, CA, USA) to assess purity, while integrity and concentration were measured with the RNA Nano 6000 Assay Kit on the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). After confirming the RNA quality, the MGIEasy RNA library preparation kit was used for library construction. mRNA was enriched and purified using oligo (dT) magnetic beads, then fragmented and reverse transcribed into double-stranded cDNA with random primers. The cDNA underwent end repair, dA-tailing, adapter ligation, and PCR amplification. Following amplification, the product was denatured and cyclized into single-stranded DNA. DNA nanoballs (DNBs) were then generated through rolling circle amplification (RCA) and loaded onto sequencing chips using an automated system. Sequencing was conducted by Wuhan Onemore-tech Co., Ltd. (Wuhan) on the MGI DNBSEQ-T7 platform, utilizing paired-end 150 base-pair reads (PE150). The Combinatorial Probe Synthesis method was used, and optical signals were captured by a high-resolution imaging system to obtain the sequencing data. Bioinformatics Analysis of RNA-seq data Sequencing adapters and low-quality reads were removed using fastp (version 0.23.4)[ 24 ], and the quality of the raw reads was assessed with FastQC (version 0.12.1, Babraham Bioinformatics, UK). The cleaned reads were aligned to the Nipponbare rice reference genome (MSU_v7.0) using Hisat2 (version 2.2.1)[ 25 ], and mapping statistics were generated with Samtools (version 1.6)[ 26 ]. Gene expression levels were quantified as transcripts per million (TPM) using a custom R script (R version 4.3.1). Differentially expressed genes (DEGs) were identified with DESeq2[ 27 ]. Gene Ontology (GO) enrichment analysis was conducted using agriGO v2.0[ 28 ] and clusterProfiler 4.0[ 29 ], based on the GO annotations from the MSU7.0 gene ID (TIGR). KEGG pathway enrichment was performed using KOBAS 3.0[ 30 ], and the results were visualized with clusterProfiler 4.0[ 29 ]. Gene symbols with known or unknown functions were annotated using China Rice Data Center ( https://www.ricedata.cn/gene/ ) and funRiceGenes ( https://funricegenes.github.io/ ) [ 31 ]. The raw read counts for the time-course transcriptomes (GSE114188) were processed using the same bioinformatics pipeline, while different rhythmic genes (DRGs) were identified and analyzed primarily with the Diffcircapipeline package in R (version 4.3.1)[ 32 ]. The previously published time-course samples for GL and OE5 included six time points (4:00, 8:00, 12:00, 16:00, 20:00, and 0:00), with three replicates at each time point, collected after 45 days of growth under natural long-day conditions [ 15 ]. DNA affinity purification sequencing DNA affinity purification sequencing (DAP-seq) is an in vitro technique used to study protein-DNA interactions and identify binding motifs efficiently by synthesizing proteins in vitro. In this study, a genomic DNA (gDNA) library was created from postharvest leaves of NIL- OsPRR37 . The DAP reaction was carried out as previously described, using fragmented gDNA (100–400 bp) prepared with the Bioruptor Plus. The DNA fragments were end-repaired, 3’A-tailed, and ligated to P5 and P7 adaptors. The Halo- OsPRR37 vector was constructed by cloning the full-length OsPRR37 open reading frame into the pFN19K HaloTag® T7 SP6 Flexi® Vector. Recombinant Halo- OsPRR37 protein was expressed following the protocol of the TNT SP6 Coupled Wheat Germ Extract System (Promega, Fitchburg, USA). The expressed protein was bound to Halo-Tag ligand-coupled magnetic beads, purified using 50 µl equilibration buffer, confirmed via Western blot, and quantified using semi-quantitative dot blot analysis. The genomic DNA library was incubated with the Halo- OsPRR37 protein at room temperature for one hour. Following the incubation, the DNA bound to Halo- OsPRR37 was eluted, recovered, and amplified using PCR. The enriched DNA fragments were sequenced on an Illumina Novoseq 6000 by BIORUN Biotechnology Co., Ltd (Wuhan, China). Bioinformatics Analysis of DAP-seq data The sequencing data underwent quality assessment and filtering using FastQC (version 0.12.1, Babraham Bioinformatics, UK) and fastp (version 0.23.4)[ 24 ], respectively. The clean data were aligned to the Rice Genome Annotation Project Release 7 (MSU7) using BWA (version 0.7.17-r1188)[ 33 ]. The complexity of the sequencing library was evaluated with Preseq (version 2.0.3). Deduplication and evaluation of library insert size were performed with Picard (version 2.27.5). Peak calling was conducted to identify DNA fragments that interact with transcription factors across the genome. Peaks with a q -value 3 were identified using MACS2 (version 2.1.4)[ 34 ]. Regions extending 2 kb upstream and downstream of the transcriptional start (TSS) and termination sites (TES) were analyzed using deepTools (version 3.5.12.0)[ 35 ]. The experiment included two biological replicates, with genomic DNA serving as the control. Overlapping peaks between the replicates were identified using bedtools (version 2.30.0)[ 36 ], and subsequently, the overlapping peaks were annotated using ChIPseeker (version 1.38.0) [ 37 ] and custom scripts. DNA binding motifs were identified using the MEME Suite (v5.5.0) [ 38 ]. De novo motif discovery was performed on peak sequences under the zoops mode (zero or one occurrence per sequence), searching for up to 100 motifs (width range: 4–12 bp; E-value threshold: 0.05). These motifs were compared to known transcription factor (TF) binding sites in the JASPAR database using TOMTOM ( p < 0.05) to identify putative regulators [ 39 ]. Finally, FIMO scanned the original peaks for significant occurrences of the predicted motifs ( q -value < 0.05), retaining high-confidence matches for downstream analysis. Yeast Two-Hybrid Library Screening Yeast two-hybrid (Y2H) screening was performed to identify protein-protein interactions using a cDNA library from Oryza sativa L. cv. 9311. The Y2HGold yeast strain was transformed with a bait construct containing OsPRR37 fused to the GAL4 DNA-binding domain (BD) in the pGBKT7 vector (Clontech). The transformation was carried out following the manufacturer's protocol using the Matchmaker GAL4 Two-Hybrid System 3. The pre-transformed cDNA library of 9311, cloned into the pGADT7 vector (which contains the GAL4 activation domain, AD), was used as the prey. The bait strain was co-transformed with the prey plasmid library. Positive clones were selected on synthetic dropout (SD) medium lacking leucine, tryptophan, and histidine (SD/-Leu/-Trp/-His), supplemented with X-α-Gal and Aureobasidin A to monitor HIS3 and ADE2 reporter gene activation. After 3–5 days of incubation at 30°C, colonies that grew on selective medium and turned blue (indicating X-α-Gal hydrolysis) were identified as potential interactors. These colonies were further analyzed by plasmid extraction and sequencing of the prey constructs to identify the interacting proteins. The identified sequences were compared to databases such as NCBI and UniProt for functional annotation. Based on the sequencing results, eleven clones were selected, and their corresponding yeast strains were cultured overnight in YPDA medium. Yeast plasmid extraction was then performed. The extracted plasmids were transformed into DH5α competent cells and plated on LB + Amp plates. Single colonies were picked, plasmids re-extracted, and co-transformed with pGBKT7-OsPRR37 into the AH109 yeast strain. A spot assay was conducted to further validate the protein-protein interaction. Protein-Protein Interaction Prediction The potential interaction between OsPRR37 and its target proteins was predicted using ColabFold ( https://github.com/YoshitakaMo/localcolabfold ), a platform based on AlphaFold2 and AlphaFold-Multimer [ 40 ]. For local computation, LocalColabFold was deployed on a Linux system. The amino acid sequences of the two proteins were obtained in FASTA format and separated by a colon to indicate a dimeric complex. Predictions were performed using five models and 20 iterations to enhance accuracy. The resulting models were evaluated based on the Predicted Template Modeling score (pTM) and Interface Predicted Template Modeling score (ipTM). Models with a combined score (pTM + ipTM) greater than 0.75 were considered to indicate strong interaction capabilities [ 41 ]. The predicted structures were further analyzed and visualized using UCSF ChimeraX Tools [ 42 ]. Results Loss of OsPRR37 Function Alters Multiple Agronomic Traits in Rice To investigate the regulatory network and identify potential targets of the circadian clock gene OsPRR37 , a nearly isogenic line of OsPRR37 (NIL37) was generated by backcrossing Guangluai4 (GL) with Teqing [ 43 ], retaining a genomic fragment containing the OsPRR37 locus from Teqing in the GL background. To eliminate the effect of nearby linked genes and mimic the truncated OsPRR37 protein in GL (Fig. 1 A), a CRISPR/Cas9 guide sequence targeting the 1,497–1,516 bp coding region was used to knock out OsPRR37 in NIL37. Thirteen transgenic lines with various mutations were obtained, including two knock-out lines (KO42 and KO27) with deletions of 11 bp (1,510–1,520 bp) and 1 bp (1,511 bp), resulting in a premature stop codon (TAG) at position 1,533 bp, similar to GL (Fig. 1 B). The homozygous T2 progeny of these lines were further analyzed for phenotypic traits. Compared to NIL37, the knock-out lines showed significant reductions in days to heading, plant height, panicle length, and spikelets per panicle, with phenotypes partially recovering to resemble those of GL (Fig. 1 C-E). These findings indicate that the loss of OsPRR37 function results in widespread alterations in rice yield-related agronomic traits, suggesting a significant pleiotropic effect of OsPRR37 . To further explore the molecular mechanisms driving these phenotypic changes, multi-omics analysis was conducted. Transcriptomic Profiling of OsPRR37 Knockout Reveals Differential Gene Expression Patterns RNA-seq analysis was performed on three biological replicates of KO42 and NIL37 plant seedlings grown under natural long-day conditions. The total number of sequencing reads averaged 22,311,280, ranging from 20,315,051 to 24,901,303 ( Table S1 ). The percentage of uniquely mapped reads averaged 93.62%, with a mean alignment rate of 94.67%. With this high-quality data, Transcripts Per Million (TPM) values were calculated to measure gene expression levels. The TPM distribution among samples was relatively consistent, though a principal component analysis (PCA) plot indicated biological differences between the KO42 and NIL37 groups ( Figure S1 ). A total of 730 upregulated and 1,151 downregulated genes were identified between KO42 and NIL37 (Fig. 2 A-B). Gene ontology (GO) enrichment analysis revealed that the differentially expressed genes (DEGs) were primarily involved in photosynthesis and related to DNA binding and ion binding activities (Fig. 2 C-D). KEGG pathway enrichment analysis showed significant involvement of DEGs in various metabolic pathways, including phenylpropanoid biosynthesis, carbon metabolism, and MAPK signaling ( Figure S2 ). These results highlight the DEGs and their associated processes or pathways affected by the knockout of OsPRR37 , although the specific regulatory levels of these DEGs remain unclear. Comparative Transcriptome Analysis Reveals Extensive Downstream Effects of OsPRR37 Overexpression and Knockout To assess the regulatory network and identify downstream targets of the circadian clock gene OsPRR37 , we analyzed the differentially rhythmic genes (DRGs) by re-evaluating previously reported diurnal transcriptomes from the OsPRR37 overexpression line (OE5) and its recipient Guangluai4 (GL). Using a combination of DESeq2 and the DiffCircaPipeline package, we identified 451 differentially expressed genes (DEGs) and 6,678 DRGs. Among the DEGs, 307 overlapped with DRGs, showing rhythmic patterns in either OE5 or GL (Figure S3A), while the 144 unique DEGs exhibited arrhythmic behavior in both groups (Figure S3B). DiffCircaPipeline categorized DRGs based on four parameters—fitness, amplitude, phase, and mean difference—revealing a varying number of unique DRGs in each condition (29 to 1,280) (Fig. 3 A). Most genes with phase differences were clustered around 20:00–24:00 and 6:00–8:00 (Fig. 3 B). Clustering of unique DRGs in the fitness condition showed six distinct clusters (C1-C6), with C5 exhibiting an increase in fitness, while the other clusters (C1-C4, C6) showed reduced fitness in OE5 (Fig. 3 C). In the amplitude difference group, clusters C1 and C2 displayed elevated troughs, while C3 and C5 exhibited reduced peaks and troughs. Clusters C4 and C6 showed both elevated troughs and reduced peaks, suggesting that these genes would be overlooked if only considering the mean difference between OE5 and GL (Fig. 3 D). Similar trends were observed in the mean difference, phase difference, and overall difference conditions (Figure S4A-C). These analyses resulted in a total of 6,822 DEGs and DRGs, which were used for further analysis to investigate the effects of OsPRR37 overexpression. GO enrichment analysis of differentially expressed genes (DEGs) in KO42 and OE5 highlighted distinct functional patterns. In the comparison between KO42 and NIL37, the most enriched GO terms were associated with photosynthesis (e.g., light harvesting) and ion binding (e.g., calcium ion binding), suggesting that the knockout of OsPRR37 primarily affects photosynthesis and oxidative stress responses (Fig. 2 C-D). Additionally, pathways related to glutathione metabolism and MAPK signaling were enriched, implying that OsPRR37 plays a role in regulating stress responses and metabolic processes under stress conditions. In contrast, the OE5 vs GL comparison revealed enrichment in terms related to carbohydrate metabolism (e.g., carbohydrate metabolic process), sequence-specific DNA binding, and ATP hydrolysis activity (Figure S5A-B), indicating that OsPRR37 overexpression has an impact on energy metabolism, particularly in regulating carbohydrate usage and ATP production. KEGG pathway analysis provided further insights into the biological processes influenced by OsPRR37 . In KO42 vs NIL37, the most enriched pathways were metabolic pathways, secondary metabolite biosynthesis, and phenylpropanoid biosynthesis, reflecting significant effects on plant metabolism and secondary metabolite production (Figure S2 ). Enriched pathways such as carbon fixation and MAPK signaling emphasized OsPRR37 ’s role in regulating photosynthesis and stress signaling. Conversely, in the OE5 vs GL comparison, enriched pathways included amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, and glycolysis/gluconeogenesis, all crucial for energy production (Figure S5C). These findings suggest that OsPRR37 overexpression influences the regulation of energy metabolism, particularly processes related to carbohydrate metabolism and ATP generation, with additional pathways like fatty acid degradation further supporting this shift. In summary, GO and KEGG enrichment analyses reveal that OsPRR37 knockout (KO42) primarily affects photosynthesis, stress responses, and secondary metabolism, whereas its overexpression (OE5) mainly modulates energy metabolism, particularly carbohydrate metabolism and ATP production. Negative Correlation Filtering Reveals Coherently Regulated Targets of OsPRR37 To distinguish between direct regulatory targets and indirect or diffuse response genes affected by OsPRR37 , we compared transcriptomic profiles from OsPRR37 overexpression (OE5 vs. GL) and knockout (KO42 vs. NIL37) lines. This analysis identified 652 overlapping differentially expressed genes (DEGs) (Fig. 4 A). Of these, 454 genes exhibited negatively correlated expression patterns—i.e., upregulated in one group and downregulated in the other—suggesting they are coherently regulated targets of OsPRR37 (Fig. 4 B). Notably, this coherently regulated set includes RFT1 , a well-known florigen [ 44 , 45 ], and OsMADS1 , a key regulator of floral development [ 46 , 47 ], supporting their intermediate roles in the OsPRR37 -mediated regulation of flowering time and panicle architecture. Circadian phase analysis of these negatively correlated DEGs revealed a dominant expression peak from 16:00 to 23:00, with two smaller peaks at 01:00–02:00 and around 10:00 (Fig. 4 C). This indicates that most genes are activated during the evening, while a minority are morning-phased. Cluster analysis further resolved this group into four distinct expression patterns. Cluster C1, enriched for morning-phased genes, showed slightly reduced expression in OE5. Cluster C2 exhibited dampened amplitude without a change in median expression, likely representing buffered responses. In contrast, Clusters C3 and C4—comprising 321 genes (70.7% of the set)—displayed markedly increased expression in OE5 (Fig. 4 D). Gene ontology and pathway enrichment analysis highlighted significant terms related to photosynthesis, light harvesting, and primary metabolism (Figure S6A-B), consistent with the enrichment patterns observed in the KO42 vs. NIL37 comparison (Fig. 2 C and Figure S2 ). Functional network analysis of biological processes (Fig. 4 E) revealed that five morning-phased genes— CP24 , LOC_Os06g21590 , LOC_Os02g10390 , LYL1 / OsChIP / OsGGR , and PNZIP / OsCRD1 / YL-1 / YGL8 —are involved in photosynthesis and chlorophyll biosynthesis. These genes were consistently repressed in OE5 and induced in KO42 (Fig. 4 F-G). Specifically, CP24 encodes a chlorophyll a–b binding protein, LYL1 a geranylgeranyl reductase, and PNZIP a subunit of magnesium-protoporphyrin IX monomethyl ester cyclase essential for chlorophyll biosynthesis. In contrast, four evening-phased genes enriched in carbohydrate and L-serine biosynthetic processes were upregulated in OE5 and downregulated in KO42, underscoring a temporal division of OsPRR37 ’s regulatory influence. Positively Correlated Genes Represent Diffuse Transcriptional Responses to OsPRR37 Perturbation A total of 198 overlapping DEGs displayed positively correlated changes in expression between the OE5 vs. GL and KO42 vs. NIL37 comparisons (Figure S7A). This expression pattern suggests that these genes are not directly regulated by OsPRR37 but rather represent downstream or indirect effects resulting from its perturbation. Unlike the negatively correlated, coherently regulated targets, these genes lacked a consistent circadian phase preference. Their expression peak times were broadly distributed, except for a sharp enrichment around 23:00 (Figure S7B), further supporting a more diffuse and asynchronous regulatory pattern. Hierarchical clustering revealed six expression clusters. Cluster C1 exhibited reduced expression in OE5, while Clusters C2 through C6 showed increased expression, with varying degrees of rhythmicity (Figure S7C). Notably, Cluster C6 displayed strongly elevated but non-rhythmic expression, highlighting possible deregulation rather than time-of-day-dependent control. Gene ontology and pathway enrichment analysis revealed significant associations with primary carbohydrate metabolism (including starch and sucrose metabolism), biosynthesis of secondary metabolites (e.g., phenylpropanoids, carotenoids), and responses to oxidative stress (Figure S6C-D). These functional enrichments indicate that OsPRR37 perturbation may broadly impact energy distribution and defense preparedness in rice. Further network analysis identified Os9BGlu31 as the only previously characterized gene in this group. It encodes a glycoside hydrolase family GH1 enzyme involved in modulating levels of phenolic acids and carboxylated phytohormones via transglycosylation (Figure S7D-F). The presence of such stress- and metabolism-related genes reinforces the interpretation that these positively correlated genes function in buffering physiological homeostasis under OsPRR37 perturbation, defining the Diffuse Response Layer. Identification of Direct OsPRR37 Targets Through Integrated DAP-seq and Transcriptomic Analysis While transcriptomic comparisons between OsPRR37 overexpression (OE5) and knockout (KO42) lines helped identify coherently regulated genes, DAP-seq was employed to pinpoint direct DNA-binding targets of OsPRR37. Using stringent peak-calling criteria ( q -value 3), we identified 3,924 and 5,416 peaks in two independent biological replicates, respectively. A total of 1,679 overlapping peaks were retained for further analysis. Genomic feature annotation revealed that 31.56% of these peaks were located in promoter regions, and 21.5% were in distal intergenic regions (Fig. 5 A). The remaining peaks, found in 5’ UTRs, 3’ UTRs, exons, introns, and downstream regions, were collectively classified as genebody-associated in this study. Among the genes associated with these peaks, sixteen overlapped with differentially expressed genes (DEGs) from both OE5 vs. GL and KO42 vs. NIL37 comparisons (Fig. 5 B). Of these sixteen genes, nine displayed coherent expression changes between overexpression and knockout lines (Fig. 5 C). De novo motif discovery using the MEME Suite identified putative DNA-binding motifs for six out of these nine coherently regulated genes. These motifs were located in promoter regions (2 genes), gene bodies (3 genes), or intergenic regions (1 gene) (Fig. 5 D). From the full set of nine coherent targets, four representative genes—LOC_Os03g09120, SP1, OsHAK12, and OsCML4—were selected for further analysis based on their known or predicted biological functions and, where applicable, the presence and location of DAP-seq–derived motifs. The de novo motifs identified in these genes were subsequently compared to known transcription factor (TF) binding sites using the JASPAR plant database (Table S2 ). The promoter of LOC_Os03g09120 contained two motifs, YYTYTCYYYCTC and RARGARRAGRAR. The intergenic region upstream of SP1 harbored GRGAGARRAGR, while the gene body of OsHAK12 contained GAAGAAGADRAD (Fig. 5 E). No conserved motif was found in the promoter region of OsCML4 . Among these, the motif YYTYTCYYYCTC matched three known TF binding motifs: MA1404.1 ( BPC1 ), MA1402.1 ( BPC6 ), and MA1416.1 ( RAMOSA1 ). BPC1 and BPC6 , members of the BBR / BPC transcription factor family, are implicated in cytokinin signaling, suggesting that this motif may mediate regulation of cytokinin-responsive genes. The match with RAMOSA1 —known for its role in inflorescence architecture and meristem determinacy—further suggests involvement of this motif in developmental gene repression through complexes possibly including RAMOSA1 and co-repressors such as REL2. Thus, YYTYTCYYYCTC may act as a shared regulatory element in both developmental and circadian gene networks. Rhythmic expression analysis revealed differential responses of these target genes to OsPRR37 overexpression. LOC_ Os03g09120 and SP1 were repressed in OE5, whereas OsHAK12 and OsCML4 were activated (Fig. 5 F). Notably, SP1 peaked in expression between 04:00 and 07:00, aligning with dawn and exhibiting an antiphase relationship with OsPRR37 , which peaks around dusk (18:00). This complementary pattern suggests that SP1 is a direct transcriptional target of OsPRR37. Given that SP1 encodes a putative NPF (NRT1/PTR family) transporter involved in determining panicle size, it may influence reproductive development through the transport of peptides, nitrate, or hormones. These findings suggest that OsPRR37 regulates SP1 in a time-of-day-dependent manner, linking circadian control to nutrient and hormone-mediated developmental processes. The remaining three target genes exhibited peak expression around midnight. Two of them have known functions in abiotic stress responses. OsHAK12 encodes a Na⁺ transporter crucial for salt tolerance via shoot Na⁺ exclusion, and its activation in OE5 suggests OsPRR37 enhances this protective mechanism. OsCML4 , a calmodulin-like protein, is involved in calcium signaling under salt stress and is more highly expressed in salt-tolerant rice lines. Together, these findings suggest that OsPRR37 may enhance salt stress resilience by regulating both Na⁺ transport and calcium-mediated signaling pathways via OsHAK12 and OsCML4 . OsPRR37 Interacts with Proteins Involved in Key Developmental and Metabolic Pathways Although previous studies have suggested that OsPRR37 functions primarily as a transcriptional repressor, our observations indicate a more complex role. Notably, several genes—including the putative direct targets OsHAK12 and OsCML4 —were upregulated in OsPRR37 -overexpressing lines. This suggests that OsPRR37 may act not only as a repressor but also as a transcriptional activator, depending on its interaction partners. A similar dual regulatory function has been reported for another circadian clock component, OsPRR1/OsTOC1, which can either activate or repress gene expression depending on the regulatory context [ 5 ]. These findings point to the broader regulatory flexibility of circadian proteins, mediated by both protein–DNA and protein–protein interactions (PPIs). To identify potential OsPRR37-interacting proteins, a yeast two-hybrid (Y2H) screen was conducted using a cDNA library from the elite rice cultivar 9311. This screen yielded 33 positive clones (Fig. 6 A), of which 29 were successfully sequenced, resulting in 26 unique annotated genes (Table S3). To validate these interactions, 11 candidate clones were selected to co-transform with OsPRR37 into the AH109 yeast strain, followed by spot assays to confirm interaction (Fig. 6 B). A PPI network was then constructed, revealing physical associations between OsPRR37 and the 26 candidate proteins (Fig. 6 C). Remarkably, 22 of the 26 corresponding genes displayed rhythmic expression patterns in both the wild-type (GL) and OsPRR37-overexpressing (OE5) backgrounds (Fig. 6 D). Among the 26 interacting proteins identified, seven are known to influence key agronomic traits. For instance, OsGlyRS3, OsHAPL1, and OsHLS1/qHd2-1 regulate flowering time by modulating florigen pathways and gibberellin signaling [ 48 – 50 ], while CYP714B2 helps maintain hormonal balance through gibberellin metabolism [ 51 ]. OsSnRK1A is essential for sugar homeostasis under stress conditions [ 52 ], and OsFLU1 together with NAL9/VYL are involved in chlorophyll biosynthesis and chloroplast development [ 53 , 54 ]. Sugars produced through photosynthesis act as central regulators of various cellular pathways controlling plant growth and development [ 55 ]. Moreover, the circadian clock links sugar signaling with the strigolactone pathway to coordinate tiller-bud growth and panicle development, ultimately shaping plant architecture and yield [ 56 ]. Taken together, these findings indicate that OsPRR37 serves as a central hub, integrating circadian timing with developmental and metabolic networks to regulate flowering, growth, and environmental adaptation in rice. To further investigate potential interactions between OsPRR37 and its binding partners, structural predictions were performed using ColabFold [ 40 ]. Of the 26 proteins analyzed, six showed a high probability of interacting with OsPRR37, with predicted TM + ipTM scores above 0.75 (Table S4), including OsGlyRS3 (0.95) and OsSnRK1A (0.85). Structural analysis revealed that OsGlyRS3 did not directly interact with the Response Regulator Receiver Domain or the CCT motif of OsPRR37, whereas OsSnRK1A primarily interacted with the CCT motif (Fig. 6 E). Based on previous functional studies, OsGlyRS3 regulates flowering time by modulating the expression of genes such as Hd1, Hd3a, and OsMADS51 [ 49 ], while sugar starvation activates the OsSnRK1A–OsbHLH111/OsSGI1–OsTPP7 module to suppress rice growth [ 52 ]. Integrating these findings, OsGlyRS3 may modulate OsPRR37 activity by stabilizing protein–protein interactions involved in flowering pathways, whereas OsSnRK1A may influence the DNA-binding capacity of OsPRR37 through its association with the CCT motif under sugar-deficient conditions. Overall, OsPRR37 likely functions by forming complexes with proteins involved in transcriptional regulation, energy metabolism, and organelle development. Through these interactions, it integrates circadian signals with pathways controlling flowering time, sugar metabolism, chloroplast development, and gibberellin biosynthesis, thereby coordinating rice growth, stress responses, and agronomically important traits. Discussion In this study, a multi-layered regulatory model for the rice circadian clock gene OsPRR37 was established, revealing how a single clock component integrates environmental signals with the regulation of multiple agronomic traits. By integrating transcriptomic, DAP-seq, and PPI datasets, direct targets of OsPRR37 were distinguished from coherently regulated intermediates and diffuse downstream responses. OsPRR37 interacts with specific protein partners to modulate its transcriptional activity (Level 1, Modulatory Layer), directly regulates a core set of target genes through DNA binding (Level 2, Direct Target Layer), indirectly influences intermediary genes via transcriptional cascades (Level 3, Indirect Coherent Layer), and triggers broader metabolic and stress-related downstream responses (Level 4, Diffuse Response Layer). This hierarchical framework demonstrated that OsPRR37 coordinates photosynthesis, energy metabolism, and stress adaptation, highlighting its pleiotropic role in linking circadian rhythms to yield-related agronomic traits (Fig. 7 ). These findings represent a conceptual advancement in deciphering the regulatory architecture of plant circadian networks, laying a foundation for both the targeted enhancement of crop performance and the mathematical modeling of circadian clock systems. The results expanded previous knowledge of PRRs by clarifying the breadth and organization of OsPRR37 -mediated regulation. Previous studies in Arabidopsis reported that PRR5 and PRR7 repress PHYTOCHROME-INTERACTING FACTOR (PIF) activity to fine-tune shade-avoidance responses [ 18 ], while PRR1/TOC1 functions either as an activator or a repressor depending on its interaction partners [ 5 ]. Consistent with these observations, OsPRR37 displayed dual regulatory capacity, repressing genes such as SP1 and LOC_Os03g09120 while activating stress-related genes including OsHAK12 and OsCML4 (Fig. 5 C-F). This flexibility is likely driven by dynamic protein–protein interactions, as revealed by yeast two-hybrid screening and structural modeling (Fig. 6 ). This is further supported by studies in Arabidopsis showing that other core clock proteins such as CCA1, despite their high cellular abundance, bind fewer genomic targets than predicted [ 19 ]. Such findings reinforce the view that protein interactions and regulatory context play critical roles in shaping clock outputs. The hierarchical model also elucidated how OsPRR37 integrates circadian timing with key biological processes. Genes within the Indirect Coherent Layer included regulators of reproductive development ( RFT1 , OsMADS1 ) and photosynthesis, consistent with phenotypic changes in heading date, plant height, and panicle architecture observed in OsPRR37 -deficient lines (Fig. 1 C-E). Genes in the Indirect Coherent Layer exhibited negatively correlated expression changes in OsPRR37 overexpression and knockout backgrounds and were enriched in pathways related to carbohydrate metabolism and stress signaling. By contrast, genes in the Diffuse Response Layer showed broad metabolic and stress-associated signatures, likely representing secondary or feedback effects of OsPRR37 perturbation (Figure S6). A similar regulatory strategy has been reported for OsPRR95, which establishes a feedback loop with ABA signaling components OsRCAR10 and OsABI5 to fine-tune seed germination and seedling growth, further illustrating how PRRs coordinate circadian timing with hormonal and stress-response pathways [ 58 ]. This stratified framework clarified by which circadian signals propagate through transcriptional cascades and metabolic networks to influence growth and stress resilience. In addition to providing mechanistic insight, this study highlights the broader agronomic importance of OsPRR37 . Knockout of OsPRR37 altered multiple yield-related traits, including flowering time, plant height, and panicle architecture (Fig. 1 ), and also disrupted pathways linked to photosynthetic efficiency and salt stress responses (Fig. 2 C, D). Conversely, OsPRR37 overexpression primarily affected energy metabolism, particularly carbohydrate utilization and ATP production (Figure S5). These pleiotropic effects position OsPRR37 as a central regulatory node that coordinates energy allocation and developmental timing under fluctuating environmental conditions. Supporting this notion, recent studies have shown that targeted editing of the promoter and distal regulatory regions of OsPRR37 can fine-tune its expression, optimizing heading date and increasing grain yield without compromising varietal quality [ 59 ]. This evidence further highlights the translational potential of the regulatory network uncovered in this study.. A similar role has been attributed to OsPRR73 , another rice PRR that connects the circadian clock to the photoperiodic flowering pathway by directly repressing the floral inducer Ehd1 and the circadian gene OsCCA1 [ 60 ]. Beyond flowering regulation, OsPRR73 also enhances salt tolerance by transcriptionally repressing the Na⁺ transporter gene OsHKT2;1 through recruitment of the co-repressor HDAC10, thereby maintaining sodium homeostasis under salt stress [ 61 ]. In this study, five morning-phase genes, which are coherently regulated, were identified as being involved in photosynthesis and chlorophyll biosynthesis (Fig. 4 E-G). Furthermore, direct targets of OsPRR37, including OsHAK12 and OsCML4, encode a Na⁺ transporter and a calmodulin-like protein, respectively, both of which are important for salt stress responses (Fig. 5 C-F). Recent study has demonstrated that salt stress negatively impacts photosynthetic efficiency and inhibits plant growth by reducing chlorophyll content, decreasing photosynthetic gas exchange, and inducing photoinhibition [ 62 ]. These findings suggest that OsPRR37 plays a key role in integrating salt stress signaling with photosynthesis pathways. Furthermore, this study highlights the broader importance of PRRs in linking circadian rhythms with developmental processes and stress responses. The hierarchical model proposed here provides a framework for optimizing yield-related traits by targeting OsPRR37 or its downstream genes, offering promising prospects for developing climate-resilient rice varieties. Although integrating multi-omics datasets allowed the OsPRR37 regulatory model to be constructed at high resolution, several questions remain. The direct binding events identified by DAP-seq require in planta validation using approaches such as ChIP-seq or CRISPR-based motif editing. Similarly, the functional relevance of specific protein–protein interactions should be confirmed through genetic analyses. Given that circadian regulation is dynamic and strongly influenced by the environment, future time-course multi-omics experiments performed under realistic field conditions will be essential to further refine and validate this regulatory model. Inspired by recent single-cell multi-omics studies in rice [ 63 ], future research could combine time-course sampling with single-cell and spatially resolved multi-omics analyses to define organ- and cell-type-specific regulatory differences at an unprecedented resolution. Such spatiotemporal analyses would greatly deepen mechanistic understanding and provide new molecular targets for breeding climate-resilient, high-yielding rice varieties. Conclusion This study demonstrates that OsPRR37 functions as a central regulatory hub within the rice circadian clock, orchestrating flowering time, chloroplast function, energy metabolism, and stress responses through a hierarchical, multi-layered network. Through the integration of transcriptomic, DNA-binding, and protein–protein interaction datasets, the analysis identified direct targets, intermediate regulators, and broad downstream pathways under the control of OsPRR37. This multi-omics framework illustrates how circadian clock components translate environmental timing cues into developmental and metabolic decisions that ultimately influence yield-related traits. The proposed hierarchical regulatory model of OsPRR37 significantly advances the understanding of plant circadian network architecture and its pleiotropic effects on agronomic performance. These findings establish a robust foundation for precision breeding strategies that fine-tune circadian clock components to enhance yield stability and stress resilience in rice and other crop species. Declarations Data Availability Statement The RNA-seq and DAP-seq raw data supporting the conclusions of this article have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1294410. Author Contributions C.L.: Conceptualization, Methodology, Software, Writing - Original Draft, Writing - Review & Editing, Funding acquisition. L.L.: Software, Formal analysis, Investigation. Y.L.L.: Data Curation, Project administration. Y.H.L.: Data Curation, Writing - Review & Editing. Y.L.: Formal analysis. J.D.: Data Curation, Writing - Review & Editing. X.F.Q.: Investigation, Resources, Project administration. N.L.: Conceptualization, Software, Writing - Review & Editing, Supervision, Funding acquisition. All authors read and approved the final manuscript. Funding The work was financially supported by the Chongqing Natural Science Foundation (Grant Nos. CSTB2023NSCQ-MSX0582 and cstc2019jcyj-msxmX0274) and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202300616). Acknowledgments The authors sincerely thank Dr. Yang He of the Peking University Yangtze Center for Future Health Technology for insightful advice on protein structural analysis. We also thank Prof. Kunxian Shu of Chongqing University of Posts and Telecommunications for valuable suggestions on data analysis. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. This study focuses exclusively on molecular regulatory mechanisms in rice and does not involve human participants, human data, human tissue, or animal experiments; hence, clinical trial registration is not required. References Dwivedi SL, Quiroz LF, Spillane C, Wu R, Mattoo AK, Ortiz R. Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops. Planta. 2024;259:72. https://doi.org/10.1007/s00425-023-04324-8 . Li J, Yang M, Zeng J, Chen L, Huang W. Transcriptional activation and repression in the plant circadian clock: revisiting core oscillator feedback loops and output pathways. 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Clock component OsPRR73 positively regulates rice salt tolerance by modulating OsHKT2;1-mediated sodium homeostasis. EMBO J. 2021;40:e105086. https://doi.org/10.15252/embj.2020105086 . Deng R, Li Y, Feng N-J, Zheng D-F, Khan A, Du Y-W, et al. Integrative analysis of transcriptome and metabolome reveal molecular mechanism of tolerance to salt stress in rice. BMC Plant Biol. 2025;25:335. https://doi.org/10.1186/s12870-025-06300-8 . Wang X, Huang H, Jiang S, Kang J, Li D, Wang K, et al. A single-cell multi-omics atlas of rice. Nature. 2025. https://doi.org/10.1038/s41586-025-09251-0 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.pdf Figure S1. TPM distribution and principal component analysis (PCA) of KO42 and NIL37 samples. Figure S2. KEGG pathway enrichment analysis of differentially expressed genes (DEGs) in the OsPRR37 knockout line. Figure S3. Analysis of DEGs in OsPRR37 overexpression lines. Figure S4. Analysis of rhythmically expressed genes (DRGs) in OsPRR37 overexpression lines. Figure S5. Enrichment analysis of differentially and rhythmically expressed genes (6,882 DRGs) in OsPRR37 overexpression lines. Figure S6. Enrichment analysis of negatively and positively correlated DEGs upon OsPRR37 knockout and overexpression. Figure S7. Characterization of positively correlated DEGs following OsPRR37 knockout and overexpression. SupplementaryMaterial2.xlsx Table S1. Alignment and assignment statistics for RNA-seq. Table S2. Motifs identified in the potential target genes of OsPRR37. Table S3. Sequence annotations of OsPRR37-interacting proteins identified by yeast two-hybrid screening. Table S4. ColabFold-based predictions of putative OsPRR37-interacting proteins. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 26 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers invited by journal 07 Sep, 2025 Editor invited by journal 05 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7366051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511989328,"identity":"06b04ffc-0555-45a4-bdfd-b47cbaecf298","order_by":0,"name":"Chuan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACPmYGBmYQgx8udICAFjaYFskGEJlAjBYGqBaDA0RrYecx/FzYZpdnfPzwNunCHwxyfDcSGD8X4HUYj7H0zLbkYrMzaWXSMxIYjCVvJDBLz8CvxYyZt405cdsNHjNpngSGxA03EoCChLXUJ26eAdFST6yWw4kbJCBaEgwIa2ErluY5dzxxxpm0YusZaRKGM888bJbGp4Wf//DGzzxl1Yn97Yc33i6wsZHnO5588DM+LcjAABhBEkCasYFIDRAto2AUjIJRMAowAQCwVD02KHrGSwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Liu","suffix":""},{"id":511989329,"identity":"0db743a3-3fd6-46a0-9c9a-8e811d92dc27","order_by":1,"name":"Lu Liu","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Liu","suffix":""},{"id":511989330,"identity":"7702b0ab-1a36-42bc-9d3d-30ed66f775ac","order_by":2,"name":"Yilong Liang","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yilong","middleName":"","lastName":"Liang","suffix":""},{"id":511989331,"identity":"e6b2c4bf-14b4-4175-9977-a388105bf53a","order_by":3,"name":"Yinghong Li","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yinghong","middleName":"","lastName":"Li","suffix":""},{"id":511989332,"identity":"0b2deb9d-5597-4982-95c1-b5d5ef54e978","order_by":4,"name":"Ying Liu","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":511989333,"identity":"b61af39c-450c-44e8-b879-94f697a01a1a","order_by":5,"name":"Jin Dai","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Dai","suffix":""},{"id":511989334,"identity":"01f63776-0410-477e-b2ec-879c444fe899","order_by":6,"name":"Xuefeng Qu","email":"","orcid":"","institution":"Dongguan Research Center of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Qu","suffix":""},{"id":511989335,"identity":"6c4c0b64-c715-4179-b6c6-fa4a452f7463","order_by":7,"name":"Na Li","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-13 14:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7366051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7366051/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-08001-8","type":"published","date":"2025-12-20T15:58:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91184776,"identity":"36e6ab3c-1cd9-4cf0-ae47-d9b146599d92","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1595971,"visible":true,"origin":"","legend":"\u003cp\u003eGeneration and characterization of \u003cem\u003eOsPRR37\u003c/em\u003e knocking out lines. \u003cstrong\u003e(A)\u003c/strong\u003e Schematic representation of the full-length OsPRR37 protein in NIL37 and the truncated osprr37 protein in GL.\u003cstrong\u003e (B)\u003c/strong\u003e PCR-based sequencing result of the Crispr/Cas9-targeted region. Red characters indicate mutated bases, and red boxes mark the premature stop codon (TAG). \u003cstrong\u003e(C, D) \u003c/strong\u003eImages of whole plants and panicles of KO42 at the maturing stage. White bars = 10 cm.\u003cstrong\u003e (E)\u003c/strong\u003e Statistical analysis of agronomic traits, including days to heading, plant height, panicle length, and spikelets per panicle, for NIL37, KO42, KO27, and GL. Different lowercase letters above the boxes denote statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/9f99f8414e0957113a380413.jpeg"},{"id":91185143,"identity":"4aa40dd1-736c-487d-b788-87ef82a08239","added_by":"auto","created_at":"2025-09-12 13:47:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2668657,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DEGs following \u003cem\u003eOsPRR37\u003c/em\u003e knockout. \u003cstrong\u003e(A)\u003c/strong\u003e Volcano plot showing DEGs between KO42 and NIL37. Red and blue dots represent up- and downregulated genes, respectively. The eight genes with the lowest adjusted p-values are labeled in each group.\u003cstrong\u003e (B) \u003c/strong\u003eHeatmap of all 1,881 DEGs between KO42 and NIL37.\u003cstrong\u003e (C)\u003c/strong\u003e Dot plot of GO enrichment analysis. \u003cstrong\u003e(D) \u003c/strong\u003eHierarchical clustering of enriched GO terms. Redundant GO terms (pairwise similarity \u0026gt; 0.7) were removed, retaining the most significant term (lowest adjusted \u003cem\u003ep\u003c/em\u003e-value). The resulting treeplot displays hierarchical clustering (average linkage, five clusters), with terms colored by adjusted \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/b8066dfaaf52acac296e9b8b.jpeg"},{"id":91184785,"identity":"b754befc-17f7-4fe6-b095-7f574bf40fb8","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7553699,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differential rhythm genes (DRGs) in \u003cem\u003eOsPRR37\u003c/em\u003e overexpression lines. \u003cstrong\u003e(A) \u003c/strong\u003eDistribution and overlap of DRGs identified using the DiffCircaPipeline.\u003cstrong\u003e(B) \u003c/strong\u003eRadar plot showing the phase distribution of 923 genes in the Phase_diff category. \u003cstrong\u003e(C) \u003c/strong\u003eClustering heatmap of 707 genes unique to the fitness difference gene set. \u003cstrong\u003e(D) \u003c/strong\u003eClustering heatmap of 238 genes unique to the amplitude difference gene set.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/73d91482dd241b40874cb001.jpeg"},{"id":91184784,"identity":"4fbc8081-04ad-4f3f-99ae-b39b13dfe5d9","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5446298,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of negatively correlated DEGs following \u003cem\u003eOsPRR37\u003c/em\u003e knockout and overexpression. \u003cstrong\u003e(A) \u003c/strong\u003eOverlap analysis of DEGs and DRGs between OE5 vs GL and KO42 vs NIL37. \u003cstrong\u003e(B)\u003c/strong\u003e Negative correlation analysis of the coherently regulated genes. \u003cstrong\u003e(C) \u003c/strong\u003eRadar plot showing the phase distribution of the coherently regulated genes. \u003cstrong\u003e(D) \u003c/strong\u003eClustering heatmap of the coherently regulated genes.\u003cstrong\u003e (E) \u003c/strong\u003eNetwork analysis of biological process (BP) terms and their associated genes. Redundant GO terms were removed using semantic similarity analysis (pairwise_termsim threshold = 0.7) and visualized with cnetplot to display representative GO categories and associated genes. Clustering heatmaps of genes in the network are shown for OE5 vs. GL \u003cstrong\u003e(F)\u003c/strong\u003e and KO42 vs. NIL37 \u003cstrong\u003e(G)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/494de0892af3cd7ce23b4d93.jpeg"},{"id":91185147,"identity":"e38f05d0-ae9a-47f2-92e1-c104f4b80cf7","added_by":"auto","created_at":"2025-09-12 13:47:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4015015,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of potential direct target genes identified by RNA-seq and DAP-seq. \u003cstrong\u003e(A)\u003c/strong\u003e Genomic feature distribution of DAP-seq peaks. \u003cstrong\u003e(B)\u003c/strong\u003e Overlap of genes identified by RNA-seq and DAP-seq.\u003cstrong\u003e (C) \u003c/strong\u003eNegative correlation analysis of potential direct target genes, grouped by motif location. \u003cstrong\u003e(D)\u003c/strong\u003e Genome browser visualization of DAP-seq peaks. Genome indicates gene structures; KO42 (up) and KO42 (down) show up- or downregulated genes (highlighted in red or blue, respectively); Peaks mark identified DAP-seq peaks; DAP_seq_rep1, DAP_seq_rep2, and DAP_seq_input represent biological replicates and background input. \u003cstrong\u003e(E) \u003c/strong\u003eMotif logos identified in different genomic features. \u003cstrong\u003e(F) \u003c/strong\u003eRhythmic expression patterns of \u003cem\u003eOsPRR37\u003c/em\u003e and its potential target genes in GL and OE5, shown as scatter plots with fitted cosinor curves. R² indicates the goodness-of-fit of the cosinor model (higher values denote stronger rhythmicity); \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 indicate significant rhythmicity; A reflects the amplitude of expression oscillation over the circadian cycle; Phase represents the timing of peak expression (0 = 4:00 AM; add 4 hours to convert phase values to clock time, e.g., phase 3 = 7:00 AM).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/07140d29f7920d3d879d1c8a.jpeg"},{"id":91184787,"identity":"b37ff508-d5fc-445a-9483-d384b5391e3a","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3449982,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of OsPRR37-interacting proteins identified through yeast two-hybrid screening. \u003cstrong\u003e(A)\u003c/strong\u003eIdentification of candidate OsPRR37-interacting proteins using Y2H screening with a rice cultivar 9311 cDNA library. \u003cstrong\u003e(B)\u003c/strong\u003e Validation of interactions by re-transforming plasmids from 11 positive clones into the AH109 yeast strain to confirm initial Y2H results.\u003cstrong\u003e(C) \u003c/strong\u003ePPI network showing associations between OsPRR37 and 26 candidate proteins identified in the Y2H screen. \u003cstrong\u003e(D)\u003c/strong\u003e Rhythmic expression profiles of OsPRR37-interacting genes in wild-type (GL) and \u003cem\u003eOsPRR37\u003c/em\u003eoverexpression (OE5) lines. \u003cstrong\u003e(E)\u003c/strong\u003e Structural models of OsPRR37 interactions with OsGlyRS3 and OsSnRK1A. Left, middle, and right panels show pLDDT-colored structures, chain/domain-colored structures, and zoomed-in views of key hydrogen bond interactions, respectively. Purple and pink chains represent OsPRR37 and its interacting partners, while orange and red domains correspond to the Response Regulator Domain and CCT motif of OsPRR37.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/962fd0119457928102124bd3.jpeg"},{"id":91186242,"identity":"84c7ed8d-88e0-434e-800c-190b45e51a2e","added_by":"auto","created_at":"2025-09-12 13:55:40","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":813065,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical regulatory model of \u003cem\u003eOsPRR37\u003c/em\u003e that coordinates multiple agronomic traits in rice. Level 1 depicts predicted protein interactors that modulate OsPRR37 transcriptional activity by activating or repressing target genes. Levels 2–4 show OsPRR37 direct DNA-binding targets, coherently regulated intermediary genes, and broadly influenced downstream responses, respectively. Functional categories of target genes are shown in shaded boxes corresponding to major biological pathways. Agronomic traits potentially regulated by \u003cem\u003eOsPRR37\u003c/em\u003eare indicated next to the rice plant illustration [57].\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/1b48d2413301f39e5a981c60.jpeg"},{"id":98814048,"identity":"fc246f67-dde0-4d89-a298-44086bb8be27","added_by":"auto","created_at":"2025-12-22 16:10:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26676178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/a102732f-a997-4a58-908e-8db48c593ce2.pdf"},{"id":91184781,"identity":"7d924c5b-3037-4ff8-930a-9a2824f24bf1","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1266327,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. TPM distribution and principal component analysis (PCA) of KO42 and NIL37 samples.\u003c/p\u003e\n\u003cp\u003eFigure S2. KEGG pathway enrichment analysis of differentially expressed genes (DEGs) in the OsPRR37 knockout line.\u003c/p\u003e\n\u003cp\u003eFigure S3. Analysis of DEGs in OsPRR37 overexpression lines.\u003c/p\u003e\n\u003cp\u003eFigure S4. Analysis of rhythmically expressed genes (DRGs) in OsPRR37 overexpression lines.\u003c/p\u003e\n\u003cp\u003eFigure S5. Enrichment analysis of differentially and rhythmically expressed genes (6,882 DRGs) in OsPRR37 overexpression lines.\u003c/p\u003e\n\u003cp\u003eFigure S6. Enrichment analysis of negatively and positively correlated DEGs upon OsPRR37 knockout and overexpression.\u003c/p\u003e\n\u003cp\u003eFigure S7. Characterization of positively correlated DEGs following OsPRR37 knockout and overexpression.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/b77c0ac75270f81967a19e27.pdf"},{"id":91184778,"identity":"8e0818b5-a3cf-44ab-8ebf-30b298a325b2","added_by":"auto","created_at":"2025-09-12 13:39:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21476,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Alignment and assignment statistics for RNA-seq.\u003c/p\u003e\n\u003cp\u003eTable S2. Motifs identified in the potential target genes of OsPRR37.\u003c/p\u003e\n\u003cp\u003eTable S3. Sequence annotations of OsPRR37-interacting proteins identified by yeast two-hybrid screening.\u003c/p\u003e\n\u003cp\u003eTable S4. ColabFold-based predictions of putative OsPRR37-interacting proteins.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7366051/v1/ae8b2904df2f65fd76e04cc4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multi-layered regulatory model uncovers the central role of OsPRR37 in coordinating multiple agronomic traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant circadian clock genes play a central role in enabling plants to adapt to fluctuating environmental conditions and sustain high productivity. Leveraging natural genetic variation in these genes and refining their regulatory mechanisms through targeted molecular strategies offers a promising approach to developing climate-resilient crops with enhanced tolerance to environmental stresses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The plant circadian clock is organized as a network of interlocked transcriptional\u0026ndash;translational feedback loops composed of transcriptional activators and repressors that regulate both the core oscillator and its output pathways [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the clock has long been considered to be dominated by repressive interactions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], recent studies have uncovered transcriptional activators and even dual-function transcription factors, revealing greater regulatory complexity than previously understood [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePseudo-response regulators (PRRs), as members of the two-component system, are central components of the circadian clock that integrate environmental signals and coordinate diverse biological processes, including mitochondrial function, metabolism, and agronomic traits such as flowering time; however, how these PRRs transduce signals to downstream target genes and pathways remains unclear [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In rice, \u003cem\u003eOsPRR37\u003c/em\u003e (also known as \u003cem\u003eGhd7.1\u003c/em\u003e, \u003cem\u003eDTH7\u003c/em\u003e or \u003cem\u003eHd2\u003c/em\u003e), a key locus underlying photoperiod sensitivity, has emerged as a major regulator linking circadian rhythms with multiple yield-related agronomic traits [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eOsPRR37\u003c/em\u003e modulates the rhythmic expression of over half of the rice transcriptome and represses key flowering genes such as \u003cem\u003eEhd1\u003c/em\u003e in an expression level-dependent manner [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Beyond transcriptional regulation, \u003cem\u003eOsPRR37\u003c/em\u003e also influences epigenetic modifications, including CG and CHG methylation changes in output genes, impacting starch metabolism, plant growth, and flowering time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent findings further revealed that \u003cem\u003eOsPRR37\u003c/em\u003e/\u003cem\u003eGhd7.1\u003c/em\u003e enhances rice eating quality by reducing grain protein content through repression of \u003cem\u003eOsAAP6\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite these advances, the downstream targets of \u003cem\u003eOsPRR37\u003c/em\u003e and its integrated regulatory network remain largely unresolved.\u003c/p\u003e\u003cp\u003eStudies of \u003cem\u003eArabidopsis\u003c/em\u003e core clock components highlight the complexity of circadian regulatory hierarchies. Genome-wide DNA-binding and expression analyses have shown that PRR5 and PRR7 directly interact with PHYTOCHROME-INTERACTING FACTOR (PIF) transcription factors at target promoters, repressing their transcriptional activation and subsequent shade-avoidance responses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More recently, quantitative studies of the core clock protein CCA1 revealed that, despite its high abundance (~\u0026thinsp;100,000 molecules per cell), its DNA binding was lower than expected, suggesting the presence of additional regulatory layers such as post-translational modifications, protein\u0026ndash;protein interactions, and chromatin-level constraints [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Yeast two-hybrid screening and multi-omics approaches, including RNA-seq and DNA affinity purification sequencing (DAP-seq), have proven highly effective in uncovering these layers by linking protein\u0026ndash;protein interactions and circadian-regulated transcription factor binding to gene expression rhythms [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Applying these approaches to \u003cem\u003eOsPRR37\u003c/em\u003e will provide valuable insight into its interaction networks, direct targets, and output pathways, ultimately enabling the construction of a comprehensive circadian clock\u0026ndash;centered regulatory model.\u003c/p\u003e\u003cp\u003eBuilding on these insights, we propose a multi-layered regulatory framework to elucidate how \u003cem\u003eOsPRR37\u003c/em\u003e coordinates downstream gene expression. At the Modulatory Layer (Level 1), OsPRR37 forms complexes with specific protein partners\u0026mdash;identified through yeast two-hybrid screening\u0026mdash;that modulate its transcriptional activity, DNA-binding specificity, and context-dependent regulatory functions. The Direct Target Layer (Level 2) consists of genes directly bound and transcriptionally regulated by OsPRR37, as identified through DAP-seq and RNA-seq.\u0026nbsp;The Indirect Coherent Layer (Level 3) encompasses genes not directly bound by OsPRR37 but consistently up- or down-regulated in both overexpression and knockout backgrounds, reflecting regulation through intermediary factors or transcriptional cascades. Finally, the Diffuse Response Layer (Level 4) includes genes with variable or non-directional expression changes following \u003cem\u003eOsPRR37\u003c/em\u003e perturbation, likely representing broader downstream effects or system-level feedback. Elucidating this multi-layered regulatory framework by integrating protein\u0026ndash;protein interaction, transcriptional, and functional genomics data will enhance our understanding of how circadian regulatory networks coordinate multiple agronomic traits.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant Materials and Growth Conditions\u003c/h2\u003e\u003cp\u003eThe nearly isogenic line of \u003cem\u003eOsPRR37\u003c/em\u003e (NIL37) was derived from the elite rice variety Guangluai 4 (GL) and contains the functional allele of \u003cem\u003eOsPRR37\u003c/em\u003e from the elite variety Teqing. Knock-out lines (KO42 and KO27) were generated using CRISPR/Cas9 technology to target the coding sequence of \u003cem\u003eOsPRR37\u003c/em\u003e in the NIL37 line. Diurnal transcriptome data previously collected from GL and the \u003cem\u003eOsPRR37\u003c/em\u003e overexpression line OE5 (where \u003cem\u003eOsPRR37\u003c/em\u003e was overexpressed in GL) were utilized [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Rice growth phenotypes were observed under natural long-day conditions in Chongqing, China.\u003c/p\u003e\u003cp\u003eFor DAP-seq analysis, NIL37 seeds were grown in a controlled growth chamber (PRX-380B, Shanghai Guning Instrument Co., Ltd.) for 15 days post-germination, under a 28\u0026deg;C environment with a 14-hour light/10-hour dark cycle (light from 6:00 AM to 8:00 PM). For RNA-seq analysis, NIL37 and KO42 seeds were grown for 15 days under natural long-day conditions in Chongqing, China. The topmost expanded leaves were collected at 8:00 AM, immediately frozen in liquid nitrogen, and stored at -80\u0026deg;C for subsequent DNA and RNA extraction. Three and two biological replicates were prepared for RNA-seq and DAP-seq analyses, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNA Library Generation and Sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from rice leaves using TRIzol Reagent (Invitrogen, CA, USA) for RNA sequencing. The RNA quality was evaluated using a NanoPhotometer\u0026reg; (IMPLEN, CA, USA) to assess purity, while integrity and concentration were measured with the RNA Nano 6000 Assay Kit on the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). After confirming the RNA quality, the MGIEasy RNA library preparation kit was used for library construction. mRNA was enriched and purified using oligo (dT) magnetic beads, then fragmented and reverse transcribed into double-stranded cDNA with random primers. The cDNA underwent end repair, dA-tailing, adapter ligation, and PCR amplification. Following amplification, the product was denatured and cyclized into single-stranded DNA. DNA nanoballs (DNBs) were then generated through rolling circle amplification (RCA) and loaded onto sequencing chips using an automated system. Sequencing was conducted by Wuhan Onemore-tech Co., Ltd. (Wuhan) on the MGI DNBSEQ-T7 platform, utilizing paired-end 150 base-pair reads (PE150). The Combinatorial Probe Synthesis method was used, and optical signals were captured by a high-resolution imaging system to obtain the sequencing data.\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis of RNA-seq data\u003c/h3\u003e\n\u003cp\u003eSequencing adapters and low-quality reads were removed using fastp (version 0.23.4)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the quality of the raw reads was assessed with FastQC (version 0.12.1, Babraham Bioinformatics, UK). The cleaned reads were aligned to the Nipponbare rice reference genome (MSU_v7.0) using Hisat2 (version 2.2.1)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and mapping statistics were generated with Samtools (version 1.6)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Gene expression levels were quantified as transcripts per million (TPM) using a custom R script (R version 4.3.1). Differentially expressed genes (DEGs) were identified with DESeq2[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Gene Ontology (GO) enrichment analysis was conducted using agriGO v2.0[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and clusterProfiler 4.0[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], based on the GO annotations from the MSU7.0 gene ID (TIGR). KEGG pathway enrichment was performed using KOBAS 3.0[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and the results were visualized with clusterProfiler 4.0[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Gene symbols with known or unknown functions were annotated using China Rice Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ricedata.cn/gene/\u003c/span\u003e\u003cspan address=\"https://www.ricedata.cn/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and funRiceGenes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://funricegenes.github.io/\u003c/span\u003e\u003cspan address=\"https://funricegenes.github.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The raw read counts for the time-course transcriptomes (GSE114188) were processed using the same bioinformatics pipeline, while different rhythmic genes (DRGs) were identified and analyzed primarily with the Diffcircapipeline package in R (version 4.3.1)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The previously published time-course samples for GL and OE5 included six time points (4:00, 8:00, 12:00, 16:00, 20:00, and 0:00), with three replicates at each time point, collected after 45 days of growth under natural long-day conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDNA affinity purification sequencing\u003c/h3\u003e\n\u003cp\u003eDNA affinity purification sequencing (DAP-seq) is an in vitro technique used to study protein-DNA interactions and identify binding motifs efficiently by synthesizing proteins in vitro. In this study, a genomic DNA (gDNA) library was created from postharvest leaves of NIL-\u003cem\u003eOsPRR37\u003c/em\u003e. The DAP reaction was carried out as previously described, using fragmented gDNA (100\u0026ndash;400 bp) prepared with the Bioruptor Plus. The DNA fragments were end-repaired, 3\u0026rsquo;A-tailed, and ligated to P5 and P7 adaptors. The Halo-\u003cem\u003eOsPRR37\u003c/em\u003e vector was constructed by cloning the full-length \u003cem\u003eOsPRR37\u003c/em\u003e open reading frame into the pFN19K HaloTag\u0026reg; T7 SP6 Flexi\u0026reg; Vector. Recombinant Halo-\u003cem\u003eOsPRR37\u003c/em\u003e protein was expressed following the protocol of the TNT SP6 Coupled Wheat Germ Extract System (Promega, Fitchburg, USA). The expressed protein was bound to Halo-Tag ligand-coupled magnetic beads, purified using 50 \u0026micro;l equilibration buffer, confirmed via Western blot, and quantified using semi-quantitative dot blot analysis. The genomic DNA library was incubated with the Halo-\u003cem\u003eOsPRR37\u003c/em\u003e protein at room temperature for one hour. Following the incubation, the DNA bound to Halo-\u003cem\u003eOsPRR37\u003c/em\u003e was eluted, recovered, and amplified using PCR. The enriched DNA fragments were sequenced on an Illumina Novoseq 6000 by BIORUN Biotechnology Co., Ltd (Wuhan, China).\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis of DAP-seq data\u003c/h3\u003e\n\u003cp\u003eThe sequencing data underwent quality assessment and filtering using FastQC (version 0.12.1, Babraham Bioinformatics, UK) and fastp (version 0.23.4)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], respectively. The clean data were aligned to the Rice Genome Annotation Project Release 7 (MSU7) using BWA (version 0.7.17-r1188)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The complexity of the sequencing library was evaluated with Preseq (version 2.0.3). Deduplication and evaluation of library insert size were performed with Picard (version 2.27.5). Peak calling was conducted to identify DNA fragments that interact with transcription factors across the genome. Peaks with a \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and fold enrichment cutoff (fe-cutoff)\u0026thinsp;\u0026gt;\u0026thinsp;3 were identified using MACS2 (version 2.1.4)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Regions extending 2 kb upstream and downstream of the transcriptional start (TSS) and termination sites (TES) were analyzed using deepTools (version 3.5.12.0)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The experiment included two biological replicates, with genomic DNA serving as the control. Overlapping peaks between the replicates were identified using bedtools (version 2.30.0)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and subsequently, the overlapping peaks were annotated using ChIPseeker (version 1.38.0) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and custom scripts.\u003c/p\u003e\u003cp\u003eDNA binding motifs were identified using the MEME Suite (v5.5.0) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. De novo motif discovery was performed on peak sequences under the zoops mode (zero or one occurrence per sequence), searching for up to 100 motifs (width range: 4\u0026ndash;12 bp; E-value threshold: 0.05). These motifs were compared to known transcription factor (TF) binding sites in the JASPAR database using TOMTOM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to identify putative regulators [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Finally, FIMO scanned the original peaks for significant occurrences of the predicted motifs (\u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), retaining high-confidence matches for downstream analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eYeast Two-Hybrid Library Screening\u003c/h2\u003e\u003cp\u003eYeast two-hybrid (Y2H) screening was performed to identify protein-protein interactions using a cDNA library from \u003cem\u003eOryza sativa\u003c/em\u003e L. cv. 9311. The Y2HGold yeast strain was transformed with a bait construct containing \u003cem\u003eOsPRR37\u003c/em\u003e fused to the GAL4 DNA-binding domain (BD) in the pGBKT7 vector (Clontech). The transformation was carried out following the manufacturer's protocol using the Matchmaker GAL4 Two-Hybrid System 3. The pre-transformed cDNA library of 9311, cloned into the pGADT7 vector (which contains the GAL4 activation domain, AD), was used as the prey. The bait strain was co-transformed with the prey plasmid library. Positive clones were selected on synthetic dropout (SD) medium lacking leucine, tryptophan, and histidine (SD/-Leu/-Trp/-His), supplemented with X-α-Gal and Aureobasidin A to monitor HIS3 and ADE2 reporter gene activation. After 3\u0026ndash;5 days of incubation at 30\u0026deg;C, colonies that grew on selective medium and turned blue (indicating X-α-Gal hydrolysis) were identified as potential interactors. These colonies were further analyzed by plasmid extraction and sequencing of the prey constructs to identify the interacting proteins. The identified sequences were compared to databases such as NCBI and UniProt for functional annotation. Based on the sequencing results, eleven clones were selected, and their corresponding yeast strains were cultured overnight in YPDA medium. Yeast plasmid extraction was then performed. The extracted plasmids were transformed into DH5α competent cells and plated on LB\u0026thinsp;+\u0026thinsp;Amp plates. Single colonies were picked, plasmids re-extracted, and co-transformed with pGBKT7-OsPRR37 into the AH109 yeast strain. A spot assay was conducted to further validate the protein-protein interaction.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProtein-Protein Interaction Prediction\u003c/h3\u003e\n\u003cp\u003eThe potential interaction between OsPRR37 and its target proteins was predicted using ColabFold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/YoshitakaMo/localcolabfold\u003c/span\u003e\u003cspan address=\"https://github.com/YoshitakaMo/localcolabfold\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a platform based on AlphaFold2 and AlphaFold-Multimer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For local computation, LocalColabFold was deployed on a Linux system. The amino acid sequences of the two proteins were obtained in FASTA format and separated by a colon to indicate a dimeric complex. Predictions were performed using five models and 20 iterations to enhance accuracy. The resulting models were evaluated based on the Predicted Template Modeling score (pTM) and Interface Predicted Template Modeling score (ipTM). Models with a combined score (pTM\u0026thinsp;+\u0026thinsp;ipTM) greater than 0.75 were considered to indicate strong interaction capabilities [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The predicted structures were further analyzed and visualized using UCSF ChimeraX Tools [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eLoss of\u003c/b\u003e \u003cb\u003eOsPRR37\u003c/b\u003e \u003cb\u003eFunction Alters Multiple Agronomic Traits in Rice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the regulatory network and identify potential targets of the circadian clock gene \u003cem\u003eOsPRR37\u003c/em\u003e, a nearly isogenic line of \u003cem\u003eOsPRR37\u003c/em\u003e (NIL37) was generated by backcrossing Guangluai4 (GL) with Teqing [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], retaining a genomic fragment containing the \u003cem\u003eOsPRR37\u003c/em\u003e locus from Teqing in the GL background. To eliminate the effect of nearby linked genes and mimic the truncated OsPRR37 protein in GL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), a CRISPR/Cas9 guide sequence targeting the 1,497\u0026ndash;1,516 bp coding region was used to knock out \u003cem\u003eOsPRR37\u003c/em\u003e in NIL37. Thirteen transgenic lines with various mutations were obtained, including two knock-out lines (KO42 and KO27) with deletions of 11 bp (1,510\u0026ndash;1,520 bp) and 1 bp (1,511 bp), resulting in a premature stop codon (TAG) at position 1,533 bp, similar to GL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The homozygous T2 progeny of these lines were further analyzed for phenotypic traits. Compared to NIL37, the knock-out lines showed significant reductions in days to heading, plant height, panicle length, and spikelets per panicle, with phenotypes partially recovering to resemble those of GL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). These findings indicate that the loss of \u003cem\u003eOsPRR37\u003c/em\u003e function results in widespread alterations in rice yield-related agronomic traits, suggesting a significant pleiotropic effect of \u003cem\u003eOsPRR37\u003c/em\u003e. To further explore the molecular mechanisms driving these phenotypic changes, multi-omics analysis was conducted.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscriptomic Profiling of\u003c/b\u003e \u003cb\u003eOsPRR37\u003c/b\u003e \u003cb\u003eKnockout Reveals Differential Gene Expression Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRNA-seq analysis was performed on three biological replicates of KO42 and NIL37 plant seedlings grown under natural long-day conditions. The total number of sequencing reads averaged 22,311,280, ranging from 20,315,051 to 24,901,303 (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The percentage of uniquely mapped reads averaged 93.62%, with a mean alignment rate of 94.67%. With this high-quality data, Transcripts Per Million (TPM) values were calculated to measure gene expression levels. The TPM distribution among samples was relatively consistent, though a principal component analysis (PCA) plot indicated biological differences between the KO42 and NIL37 groups (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). A total of 730 upregulated and 1,151 downregulated genes were identified between KO42 and NIL37 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Gene ontology (GO) enrichment analysis revealed that the differentially expressed genes (DEGs) were primarily involved in photosynthesis and related to DNA binding and ion binding activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). KEGG pathway enrichment analysis showed significant involvement of DEGs in various metabolic pathways, including phenylpropanoid biosynthesis, carbon metabolism, and MAPK signaling (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). These results highlight the DEGs and their associated processes or pathways affected by the knockout of \u003cem\u003eOsPRR37\u003c/em\u003e, although the specific regulatory levels of these DEGs remain unclear.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative Transcriptome Analysis Reveals Extensive Downstream Effects of\u003c/b\u003e \u003cb\u003eOsPRR37\u003c/b\u003e \u003cb\u003eOverexpression and Knockout\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the regulatory network and identify downstream targets of the circadian clock gene \u003cem\u003eOsPRR37\u003c/em\u003e, we analyzed the differentially rhythmic genes (DRGs) by re-evaluating previously reported diurnal transcriptomes from the \u003cem\u003eOsPRR37\u003c/em\u003e overexpression line (OE5) and its recipient Guangluai4 (GL). Using a combination of DESeq2 and the DiffCircaPipeline package, we identified 451 differentially expressed genes (DEGs) and 6,678 DRGs. Among the DEGs, 307 overlapped with DRGs, showing rhythmic patterns in either OE5 or GL (Figure S3A), while the 144 unique DEGs exhibited arrhythmic behavior in both groups (Figure S3B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDiffCircaPipeline categorized DRGs based on four parameters\u0026mdash;fitness, amplitude, phase, and mean difference\u0026mdash;revealing a varying number of unique DRGs in each condition (29 to 1,280) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Most genes with phase differences were clustered around 20:00\u0026ndash;24:00 and 6:00\u0026ndash;8:00 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Clustering of unique DRGs in the fitness condition showed six distinct clusters (C1-C6), with C5 exhibiting an increase in fitness, while the other clusters (C1-C4, C6) showed reduced fitness in OE5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In the amplitude difference group, clusters C1 and C2 displayed elevated troughs, while C3 and C5 exhibited reduced peaks and troughs. Clusters C4 and C6 showed both elevated troughs and reduced peaks, suggesting that these genes would be overlooked if only considering the mean difference between OE5 and GL (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Similar trends were observed in the mean difference, phase difference, and overall difference conditions (Figure S4A-C). These analyses resulted in a total of 6,822 DEGs and DRGs, which were used for further analysis to investigate the effects of \u003cem\u003eOsPRR37\u003c/em\u003e overexpression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGO enrichment analysis of differentially expressed genes (DEGs) in KO42 and OE5 highlighted distinct functional patterns. In the comparison between KO42 and NIL37, the most enriched GO terms were associated with photosynthesis (e.g., light harvesting) and ion binding (e.g., calcium ion binding), suggesting that the knockout of \u003cem\u003eOsPRR37\u003c/em\u003e primarily affects photosynthesis and oxidative stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Additionally, pathways related to glutathione metabolism and MAPK signaling were enriched, implying that \u003cem\u003eOsPRR37\u003c/em\u003e plays a role in regulating stress responses and metabolic processes under stress conditions. In contrast, the OE5 vs GL comparison revealed enrichment in terms related to carbohydrate metabolism (e.g., carbohydrate metabolic process), sequence-specific DNA binding, and ATP hydrolysis activity (Figure S5A-B), indicating that \u003cem\u003eOsPRR37\u003c/em\u003e overexpression has an impact on energy metabolism, particularly in regulating carbohydrate usage and ATP production.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKEGG pathway analysis provided further insights into the biological processes influenced by \u003cem\u003eOsPRR37\u003c/em\u003e. In KO42 vs NIL37, the most enriched pathways were metabolic pathways, secondary metabolite biosynthesis, and phenylpropanoid biosynthesis, reflecting significant effects on plant metabolism and secondary metabolite production (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Enriched pathways such as carbon fixation and MAPK signaling emphasized \u003cem\u003eOsPRR37\u003c/em\u003e\u0026rsquo;s role in regulating photosynthesis and stress signaling. Conversely, in the OE5 vs GL comparison, enriched pathways included amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, and glycolysis/gluconeogenesis, all crucial for energy production (Figure S5C). These findings suggest that \u003cem\u003eOsPRR37\u003c/em\u003e overexpression influences the regulation of energy metabolism, particularly processes related to carbohydrate metabolism and ATP generation, with additional pathways like fatty acid degradation further supporting this shift.\u003c/p\u003e\u003cp\u003eIn summary, GO and KEGG enrichment analyses reveal that \u003cem\u003eOsPRR37\u003c/em\u003e knockout (KO42) primarily affects photosynthesis, stress responses, and secondary metabolism, whereas its overexpression (OE5) mainly modulates energy metabolism, particularly carbohydrate metabolism and ATP production.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNegative Correlation Filtering Reveals Coherently Regulated Targets of\u003c/b\u003e \u003cb\u003eOsPRR37\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo distinguish between direct regulatory targets and indirect or diffuse response genes affected by \u003cem\u003eOsPRR37\u003c/em\u003e, we compared transcriptomic profiles from \u003cem\u003eOsPRR37\u003c/em\u003e overexpression (OE5 vs. GL) and knockout (KO42 vs. NIL37) lines. This analysis identified 652 overlapping differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Of these, 454 genes exhibited negatively correlated expression patterns\u0026mdash;i.e., upregulated in one group and downregulated in the other\u0026mdash;suggesting they are coherently regulated targets of \u003cem\u003eOsPRR37\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Notably, this coherently regulated set includes \u003cem\u003eRFT1\u003c/em\u003e, a well-known florigen [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and \u003cem\u003eOsMADS1\u003c/em\u003e, a key regulator of floral development [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], supporting their intermediate roles in the \u003cem\u003eOsPRR37\u003c/em\u003e-mediated regulation of flowering time and panicle architecture.\u003c/p\u003e\u003cp\u003eCircadian phase analysis of these negatively correlated DEGs revealed a dominant expression peak from 16:00 to 23:00, with two smaller peaks at 01:00\u0026ndash;02:00 and around 10:00 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This indicates that most genes are activated during the evening, while a minority are morning-phased. Cluster analysis further resolved this group into four distinct expression patterns. Cluster C1, enriched for morning-phased genes, showed slightly reduced expression in OE5. Cluster C2 exhibited dampened amplitude without a change in median expression, likely representing buffered responses. In contrast, Clusters C3 and C4\u0026mdash;comprising 321 genes (70.7% of the set)\u0026mdash;displayed markedly increased expression in OE5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene ontology and pathway enrichment analysis highlighted significant terms related to photosynthesis, light harvesting, and primary metabolism (Figure S6A-B), consistent with the enrichment patterns observed in the KO42 vs. NIL37 comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Functional network analysis of biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) revealed that five morning-phased genes\u0026mdash;\u003cem\u003eCP24\u003c/em\u003e, \u003cem\u003eLOC_Os06g21590\u003c/em\u003e, \u003cem\u003eLOC_Os02g10390\u003c/em\u003e, \u003cem\u003eLYL1\u003c/em\u003e/\u003cem\u003eOsChIP\u003c/em\u003e/\u003cem\u003eOsGGR\u003c/em\u003e, and \u003cem\u003ePNZIP\u003c/em\u003e/\u003cem\u003eOsCRD1\u003c/em\u003e/\u003cem\u003eYL-1\u003c/em\u003e/\u003cem\u003eYGL8\u003c/em\u003e\u0026mdash;are involved in photosynthesis and chlorophyll biosynthesis. These genes were consistently repressed in OE5 and induced in KO42 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G). Specifically, \u003cem\u003eCP24\u003c/em\u003e encodes a chlorophyll a\u0026ndash;b binding protein, \u003cem\u003eLYL1\u003c/em\u003e a geranylgeranyl reductase, and \u003cem\u003ePNZIP\u003c/em\u003e a subunit of magnesium-protoporphyrin IX monomethyl ester cyclase essential for chlorophyll biosynthesis. In contrast, four evening-phased genes enriched in carbohydrate and L-serine biosynthetic processes were upregulated in OE5 and downregulated in KO42, underscoring a temporal division of \u003cem\u003eOsPRR37\u003c/em\u003e\u0026rsquo;s regulatory influence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePositively Correlated Genes Represent Diffuse Transcriptional Responses to\u003c/b\u003e \u003cb\u003eOsPRR37\u003c/b\u003e \u003cb\u003ePerturbation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 198 overlapping DEGs displayed positively correlated changes in expression between the OE5 vs. GL and KO42 vs. NIL37 comparisons (Figure S7A). This expression pattern suggests that these genes are not directly regulated by \u003cem\u003eOsPRR37\u003c/em\u003e but rather represent downstream or indirect effects resulting from its perturbation. Unlike the negatively correlated, coherently regulated targets, these genes lacked a consistent circadian phase preference. Their expression peak times were broadly distributed, except for a sharp enrichment around 23:00 (Figure S7B), further supporting a more diffuse and asynchronous regulatory pattern.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHierarchical clustering revealed six expression clusters. Cluster C1 exhibited reduced expression in OE5, while Clusters C2 through C6 showed increased expression, with varying degrees of rhythmicity (Figure S7C). Notably, Cluster C6 displayed strongly elevated but non-rhythmic expression, highlighting possible deregulation rather than time-of-day-dependent control.\u003c/p\u003e\u003cp\u003eGene ontology and pathway enrichment analysis revealed significant associations with primary carbohydrate metabolism (including starch and sucrose metabolism), biosynthesis of secondary metabolites (e.g., phenylpropanoids, carotenoids), and responses to oxidative stress (Figure S6C-D). These functional enrichments indicate that \u003cem\u003eOsPRR37\u003c/em\u003e perturbation may broadly impact energy distribution and defense preparedness in rice. Further network analysis identified \u003cem\u003eOs9BGlu31\u003c/em\u003e as the only previously characterized gene in this group. It encodes a glycoside hydrolase family GH1 enzyme involved in modulating levels of phenolic acids and carboxylated phytohormones via transglycosylation (Figure S7D-F). The presence of such stress- and metabolism-related genes reinforces the interpretation that these positively correlated genes function in buffering physiological homeostasis under \u003cem\u003eOsPRR37\u003c/em\u003e perturbation, defining the Diffuse Response Layer.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of Direct OsPRR37 Targets Through Integrated DAP-seq and Transcriptomic Analysis\u003c/h2\u003e\u003cp\u003eWhile transcriptomic comparisons between \u003cem\u003eOsPRR37\u003c/em\u003e overexpression (OE5) and knockout (KO42) lines helped identify coherently regulated genes, DAP-seq was employed to pinpoint direct DNA-binding targets of OsPRR37. Using stringent peak-calling criteria (\u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and fold enrichment\u0026thinsp;\u0026gt;\u0026thinsp;3), we identified 3,924 and 5,416 peaks in two independent biological replicates, respectively. A total of 1,679 overlapping peaks were retained for further analysis.\u003c/p\u003e\u003cp\u003eGenomic feature annotation revealed that 31.56% of these peaks were located in promoter regions, and 21.5% were in distal intergenic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The remaining peaks, found in 5\u0026rsquo; UTRs, 3\u0026rsquo; UTRs, exons, introns, and downstream regions, were collectively classified as genebody-associated in this study. Among the genes associated with these peaks, sixteen overlapped with differentially expressed genes (DEGs) from both OE5 vs. GL and KO42 vs. NIL37 comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Of these sixteen genes, nine displayed coherent expression changes between overexpression and knockout lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). De novo motif discovery using the MEME Suite identified putative DNA-binding motifs for six out of these nine coherently regulated genes. These motifs were located in promoter regions (2 genes), gene bodies (3 genes), or intergenic regions (1 gene) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). From the full set of nine coherent targets, four representative genes\u0026mdash;LOC_Os03g09120, SP1, OsHAK12, and OsCML4\u0026mdash;were selected for further analysis based on their known or predicted biological functions and, where applicable, the presence and location of DAP-seq\u0026ndash;derived motifs.\u003c/p\u003e\u003cp\u003eThe de novo motifs identified in these genes were subsequently compared to known transcription factor (TF) binding sites using the JASPAR plant database (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The promoter of \u003cem\u003eLOC_Os03g09120\u003c/em\u003e contained two motifs, YYTYTCYYYCTC and RARGARRAGRAR. The intergenic region upstream of \u003cem\u003eSP1\u003c/em\u003e harbored GRGAGARRAGR, while the gene body of \u003cem\u003eOsHAK12\u003c/em\u003e contained GAAGAAGADRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). No conserved motif was found in the promoter region of \u003cem\u003eOsCML4\u003c/em\u003e. Among these, the motif YYTYTCYYYCTC matched three known TF binding motifs: MA1404.1 (\u003cem\u003eBPC1\u003c/em\u003e), MA1402.1 (\u003cem\u003eBPC6\u003c/em\u003e), and MA1416.1 (\u003cem\u003eRAMOSA1\u003c/em\u003e). \u003cem\u003eBPC1\u003c/em\u003e and \u003cem\u003eBPC6\u003c/em\u003e, members of the \u003cem\u003eBBR\u003c/em\u003e/\u003cem\u003eBPC\u003c/em\u003e transcription factor family, are implicated in cytokinin signaling, suggesting that this motif may mediate regulation of cytokinin-responsive genes. The match with \u003cem\u003eRAMOSA1\u003c/em\u003e\u0026mdash;known for its role in inflorescence architecture and meristem determinacy\u0026mdash;further suggests involvement of this motif in developmental gene repression through complexes possibly including RAMOSA1 and co-repressors such as REL2. Thus, YYTYTCYYYCTC may act as a shared regulatory element in both developmental and circadian gene networks.\u003c/p\u003e\u003cp\u003eRhythmic expression analysis revealed differential responses of these target genes to \u003cem\u003eOsPRR37\u003c/em\u003e overexpression. LOC_\u003cem\u003eOs03g09120\u003c/em\u003e and \u003cem\u003eSP1\u003c/em\u003e were repressed in OE5, whereas \u003cem\u003eOsHAK12\u003c/em\u003e and \u003cem\u003eOsCML4\u003c/em\u003e were activated (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Notably, \u003cem\u003eSP1\u003c/em\u003e peaked in expression between 04:00 and 07:00, aligning with dawn and exhibiting an antiphase relationship with \u003cem\u003eOsPRR37\u003c/em\u003e, which peaks around dusk (18:00). This complementary pattern suggests that \u003cem\u003eSP1\u003c/em\u003e is a direct transcriptional target of OsPRR37. Given that \u003cem\u003eSP1\u003c/em\u003e encodes a putative NPF (NRT1/PTR family) transporter involved in determining panicle size, it may influence reproductive development through the transport of peptides, nitrate, or hormones. These findings suggest that OsPRR37 regulates \u003cem\u003eSP1\u003c/em\u003e in a time-of-day-dependent manner, linking circadian control to nutrient and hormone-mediated developmental processes.\u003c/p\u003e\u003cp\u003eThe remaining three target genes exhibited peak expression around midnight. Two of them have known functions in abiotic stress responses. \u003cem\u003eOsHAK12\u003c/em\u003e encodes a Na⁺ transporter crucial for salt tolerance via shoot Na⁺ exclusion, and its activation in OE5 suggests \u003cem\u003eOsPRR37\u003c/em\u003e enhances this protective mechanism. \u003cem\u003eOsCML4\u003c/em\u003e, a calmodulin-like protein, is involved in calcium signaling under salt stress and is more highly expressed in salt-tolerant rice lines. Together, these findings suggest that \u003cem\u003eOsPRR37\u003c/em\u003e may enhance salt stress resilience by regulating both Na⁺ transport and calcium-mediated signaling pathways via \u003cem\u003eOsHAK12\u003c/em\u003e and \u003cem\u003eOsCML4\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eOsPRR37 Interacts with Proteins Involved in Key Developmental and Metabolic Pathways\u003c/h2\u003e\u003cp\u003eAlthough previous studies have suggested that OsPRR37 functions primarily as a transcriptional repressor, our observations indicate a more complex role. Notably, several genes\u0026mdash;including the putative direct targets \u003cem\u003eOsHAK12\u003c/em\u003e and \u003cem\u003eOsCML4\u003c/em\u003e\u0026mdash;were upregulated in \u003cem\u003eOsPRR37\u003c/em\u003e-overexpressing lines. This suggests that OsPRR37 may act not only as a repressor but also as a transcriptional activator, depending on its interaction partners. A similar dual regulatory function has been reported for another circadian clock component, OsPRR1/OsTOC1, which can either activate or repress gene expression depending on the regulatory context [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings point to the broader regulatory flexibility of circadian proteins, mediated by both protein\u0026ndash;DNA and protein\u0026ndash;protein interactions (PPIs).\u003c/p\u003e\u003cp\u003eTo identify potential OsPRR37-interacting proteins, a yeast two-hybrid (Y2H) screen was conducted using a cDNA library from the elite rice cultivar 9311. This screen yielded 33 positive clones (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), of which 29 were successfully sequenced, resulting in 26 unique annotated genes (Table S3). To validate these interactions, 11 candidate clones were selected to co-transform with \u003cem\u003eOsPRR37\u003c/em\u003e into the AH109 yeast strain, followed by spot assays to confirm interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). A PPI network was then constructed, revealing physical associations between OsPRR37 and the 26 candidate proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Remarkably, 22 of the 26 corresponding genes displayed rhythmic expression patterns in both the wild-type (GL) and OsPRR37-overexpressing (OE5) backgrounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eAmong the 26 interacting proteins identified, seven are known to influence key agronomic traits. For instance, OsGlyRS3, OsHAPL1, and OsHLS1/qHd2-1 regulate flowering time by modulating florigen pathways and gibberellin signaling [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], while CYP714B2 helps maintain hormonal balance through gibberellin metabolism [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. OsSnRK1A is essential for sugar homeostasis under stress conditions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and OsFLU1 together with NAL9/VYL are involved in chlorophyll biosynthesis and chloroplast development [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Sugars produced through photosynthesis act as central regulators of various cellular pathways controlling plant growth and development [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, the circadian clock links sugar signaling with the strigolactone pathway to coordinate tiller-bud growth and panicle development, ultimately shaping plant architecture and yield [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Taken together, these findings indicate that OsPRR37 serves as a central hub, integrating circadian timing with developmental and metabolic networks to regulate flowering, growth, and environmental adaptation in rice.\u003c/p\u003e\u003cp\u003eTo further investigate potential interactions between OsPRR37 and its binding partners, structural predictions were performed using ColabFold [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Of the 26 proteins analyzed, six showed a high probability of interacting with OsPRR37, with predicted TM\u0026thinsp;+\u0026thinsp;ipTM scores above 0.75 (Table S4), including OsGlyRS3 (0.95) and OsSnRK1A (0.85). Structural analysis revealed that OsGlyRS3 did not directly interact with the Response Regulator Receiver Domain or the CCT motif of OsPRR37, whereas OsSnRK1A primarily interacted with the CCT motif (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Based on previous functional studies, OsGlyRS3 regulates flowering time by modulating the expression of genes such as Hd1, Hd3a, and OsMADS51 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], while sugar starvation activates the OsSnRK1A\u0026ndash;OsbHLH111/OsSGI1\u0026ndash;OsTPP7 module to suppress rice growth [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Integrating these findings, OsGlyRS3 may modulate OsPRR37 activity by stabilizing protein\u0026ndash;protein interactions involved in flowering pathways, whereas OsSnRK1A may influence the DNA-binding capacity of OsPRR37 through its association with the CCT motif under sugar-deficient conditions.\u003c/p\u003e\u003cp\u003eOverall, OsPRR37 likely functions by forming complexes with proteins involved in transcriptional regulation, energy metabolism, and organelle development. Through these interactions, it integrates circadian signals with pathways controlling flowering time, sugar metabolism, chloroplast development, and gibberellin biosynthesis, thereby coordinating rice growth, stress responses, and agronomically important traits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, a multi-layered regulatory model for the rice circadian clock gene \u003cem\u003eOsPRR37\u003c/em\u003e was established, revealing how a single clock component integrates environmental signals with the regulation of multiple agronomic traits. By integrating transcriptomic, DAP-seq, and PPI datasets, direct targets of OsPRR37 were distinguished from coherently regulated intermediates and diffuse downstream responses. OsPRR37 interacts with specific protein partners to modulate its transcriptional activity (Level 1, Modulatory Layer), directly regulates a core set of target genes through DNA binding (Level 2, Direct Target Layer), indirectly influences intermediary genes via transcriptional cascades (Level 3, Indirect Coherent Layer), and triggers broader metabolic and stress-related downstream responses (Level 4, Diffuse Response Layer). This hierarchical framework demonstrated that \u003cem\u003eOsPRR37\u003c/em\u003e coordinates photosynthesis, energy metabolism, and stress adaptation, highlighting its pleiotropic role in linking circadian rhythms to yield-related agronomic traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings represent a conceptual advancement in deciphering the regulatory architecture of plant circadian networks, laying a foundation for both the targeted enhancement of crop performance and the mathematical modeling of circadian clock systems.\u003c/p\u003e\u003cp\u003eThe results expanded previous knowledge of PRRs by clarifying the breadth and organization of \u003cem\u003eOsPRR37\u003c/em\u003e-mediated regulation. Previous studies in \u003cem\u003eArabidopsis\u003c/em\u003e reported that PRR5 and PRR7 repress PHYTOCHROME-INTERACTING FACTOR (PIF) activity to fine-tune shade-avoidance responses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while PRR1/TOC1 functions either as an activator or a repressor depending on its interaction partners [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consistent with these observations, OsPRR37 displayed dual regulatory capacity, repressing genes such as \u003cem\u003eSP1\u003c/em\u003e and \u003cem\u003eLOC_Os03g09120\u003c/em\u003e while activating stress-related genes including \u003cem\u003eOsHAK12\u003c/em\u003e and \u003cem\u003eOsCML4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). This flexibility is likely driven by dynamic protein\u0026ndash;protein interactions, as revealed by yeast two-hybrid screening and structural modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This is further supported by studies in \u003cem\u003eArabidopsis\u003c/em\u003e showing that other core clock proteins such as CCA1, despite their high cellular abundance, bind fewer genomic targets than predicted [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Such findings reinforce the view that protein interactions and regulatory context play critical roles in shaping clock outputs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe hierarchical model also elucidated how \u003cem\u003eOsPRR37\u003c/em\u003e integrates circadian timing with key biological processes. Genes within the Indirect Coherent Layer included regulators of reproductive development (\u003cem\u003eRFT1\u003c/em\u003e, \u003cem\u003eOsMADS1\u003c/em\u003e) and photosynthesis, consistent with phenotypic changes in heading date, plant height, and panicle architecture observed in \u003cem\u003eOsPRR37\u003c/em\u003e-deficient lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). Genes in the Indirect Coherent Layer exhibited negatively correlated expression changes in \u003cem\u003eOsPRR37\u003c/em\u003e overexpression and knockout backgrounds and were enriched in pathways related to carbohydrate metabolism and stress signaling. By contrast, genes in the Diffuse Response Layer showed broad metabolic and stress-associated signatures, likely representing secondary or feedback effects of \u003cem\u003eOsPRR37\u003c/em\u003e perturbation (Figure S6). A similar regulatory strategy has been reported for OsPRR95, which establishes a feedback loop with ABA signaling components OsRCAR10 and OsABI5 to fine-tune seed germination and seedling growth, further illustrating how PRRs coordinate circadian timing with hormonal and stress-response pathways [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This stratified framework clarified by which circadian signals propagate through transcriptional cascades and metabolic networks to influence growth and stress resilience.\u003c/p\u003e\u003cp\u003eIn addition to providing mechanistic insight, this study highlights the broader agronomic importance of \u003cem\u003eOsPRR37\u003c/em\u003e. Knockout of \u003cem\u003eOsPRR37\u003c/em\u003e altered multiple yield-related traits, including flowering time, plant height, and panicle architecture (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and also disrupted pathways linked to photosynthetic efficiency and salt stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). Conversely, \u003cem\u003eOsPRR37\u003c/em\u003e overexpression primarily affected energy metabolism, particularly carbohydrate utilization and ATP production (Figure S5). These pleiotropic effects position \u003cem\u003eOsPRR37\u003c/em\u003e as a central regulatory node that coordinates energy allocation and developmental timing under fluctuating environmental conditions. Supporting this notion, recent studies have shown that targeted editing of the promoter and distal regulatory regions of \u003cem\u003eOsPRR37\u003c/em\u003e can fine-tune its expression, optimizing heading date and increasing grain yield without compromising varietal quality [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This evidence further highlights the translational potential of the regulatory network uncovered in this study..\u003c/p\u003e\u003cp\u003eA similar role has been attributed to \u003cem\u003eOsPRR73\u003c/em\u003e, another rice PRR that connects the circadian clock to the photoperiodic flowering pathway by directly repressing the floral inducer \u003cem\u003eEhd1\u003c/em\u003e and the circadian gene \u003cem\u003eOsCCA1\u003c/em\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Beyond flowering regulation, \u003cem\u003eOsPRR73\u003c/em\u003e also enhances salt tolerance by transcriptionally repressing the Na⁺ transporter gene \u003cem\u003eOsHKT2;1\u003c/em\u003e through recruitment of the co-repressor HDAC10, thereby maintaining sodium homeostasis under salt stress [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In this study, five morning-phase genes, which are coherently regulated, were identified as being involved in photosynthesis and chlorophyll biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-G). Furthermore, direct targets of OsPRR37, including OsHAK12 and OsCML4, encode a Na⁺ transporter and a calmodulin-like protein, respectively, both of which are important for salt stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). Recent study has demonstrated that salt stress negatively impacts photosynthetic efficiency and inhibits plant growth by reducing chlorophyll content, decreasing photosynthetic gas exchange, and inducing photoinhibition [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These findings suggest that \u003cem\u003eOsPRR37\u003c/em\u003e plays a key role in integrating salt stress signaling with photosynthesis pathways. Furthermore, this study highlights the broader importance of PRRs in linking circadian rhythms with developmental processes and stress responses. The hierarchical model proposed here provides a framework for optimizing yield-related traits by targeting \u003cem\u003eOsPRR37\u003c/em\u003e or its downstream genes, offering promising prospects for developing climate-resilient rice varieties.\u003c/p\u003e\u003cp\u003eAlthough integrating multi-omics datasets allowed the \u003cem\u003eOsPRR37\u003c/em\u003e regulatory model to be constructed at high resolution, several questions remain. The direct binding events identified by DAP-seq require in planta validation using approaches such as ChIP-seq or CRISPR-based motif editing. Similarly, the functional relevance of specific protein\u0026ndash;protein interactions should be confirmed through genetic analyses. Given that circadian regulation is dynamic and strongly influenced by the environment, future time-course multi-omics experiments performed under realistic field conditions will be essential to further refine and validate this regulatory model. Inspired by recent single-cell multi-omics studies in rice [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], future research could combine time-course sampling with single-cell and spatially resolved multi-omics analyses to define organ- and cell-type-specific regulatory differences at an unprecedented resolution. Such spatiotemporal analyses would greatly deepen mechanistic understanding and provide new molecular targets for breeding climate-resilient, high-yielding rice varieties.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that OsPRR37 functions as a central regulatory hub within the rice circadian clock, orchestrating flowering time, chloroplast function, energy metabolism, and stress responses through a hierarchical, multi-layered network. Through the integration of transcriptomic, DNA-binding, and protein\u0026ndash;protein interaction datasets, the analysis identified direct targets, intermediate regulators, and broad downstream pathways under the control of OsPRR37. This multi-omics framework illustrates how circadian clock components translate environmental timing cues into developmental and metabolic decisions that ultimately influence yield-related traits. The proposed hierarchical regulatory model of OsPRR37 significantly advances the understanding of plant circadian network architecture and its pleiotropic effects on agronomic performance. These findings establish a robust foundation for precision breeding strategies that fine-tune circadian clock components to enhance yield stability and stress resilience in rice and other crop species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq and DAP-seq raw data supporting the conclusions of this article have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1294410.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.L.:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Software, Writing - Original Draft, Writing - Review \u0026amp; Editing, Funding acquisition. \u003cstrong\u003eL.L.:\u003c/strong\u003e Software, Formal analysis, Investigation. \u003cstrong\u003eY.L.L.:\u003c/strong\u003e Data Curation, Project administration. \u003cstrong\u003eY.H.L.:\u003c/strong\u003e Data Curation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eY.L.:\u0026nbsp;\u003c/strong\u003eFormal analysis. \u003cstrong\u003eJ.D.:\u003c/strong\u003e Data Curation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eX.F.Q.:\u003c/strong\u003e Investigation, Resources, Project administration. \u003cstrong\u003eN.L.:\u003c/strong\u003e Conceptualization, Software, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was financially supported by the Chongqing Natural Science Foundation (Grant Nos. CSTB2023NSCQ-MSX0582 and cstc2019jcyj-msxmX0274) and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202300616).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank Dr. Yang He of the Peking University Yangtze Center for Future Health Technology for insightful advice on protein structural analysis. We also thank Prof. Kunxian Shu of Chongqing University of Posts and Telecommunications for valuable suggestions on data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study focuses exclusively on molecular regulatory mechanisms in rice and does not involve human participants, human data, human tissue, or animal experiments; hence, clinical trial registration is not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDwivedi SL, Quiroz LF, Spillane C, Wu R, Mattoo AK, Ortiz R. Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops. Planta. 2024;259:72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00425-023-04324-8\u003c/span\u003e\u003cspan address=\"10.1007/s00425-023-04324-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Yang M, Zeng J, Chen L, Huang W. Transcriptional activation and repression in the plant circadian clock: revisiting core oscillator feedback loops and output pathways. 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Nature. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-025-09251-0\u003c/span\u003e\u003cspan address=\"10.1038/s41586-025-09251-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oryza sativa L., Circadian clock, Multi-omics, Gene expression regulation, Protein-protein interactions","lastPublishedDoi":"10.21203/rs.3.rs-7366051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7366051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe plant circadian clock is crucial for regulating developmental and metabolic processes, enabling crops to adapt to environmental changes and maintain high productivity. In rice, the clock gene \u003cem\u003eOsPRR37\u003c/em\u003e plays a pivotal role in photoperiod sensitivity and the regulation of yield-related traits. However, the complete regulatory network of \u003cem\u003eOsPRR37\u003c/em\u003e remains largely unexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThis study utilized an integrated multi-omics approach, combining transcriptome profiling, DNA affinity purification sequencing (DAP-seq), and protein–protein interaction (PPI) mapping to construct a multi-layered regulatory model of \u003cem\u003eOsPRR37\u003c/em\u003e. CRISPR/Cas9 knockout lines showed significant changes in flowering time, plant height, panicle architecture, and spikelet number. Transcriptome analysis associated \u003cem\u003eOsPRR37\u003c/em\u003e with pathways related to photosynthesis, carbohydrate metabolism, and stress responses. Comparative analysis of knockout and overexpression datasets identified 454 candidate target genes exhibiting inverse expression patterns, including regulators of flowering and chlorophyll biosynthesis. DAP-seq revealed 1,679 high-confidence DNA-binding sites, with nine genes identified as direct targets, six of which contained conserved motifs associated with cytokinin signaling, inflorescence architecture, and meristem determinacy. PPI mapping through a yeast two-hybrid screen identified 26 interacting proteins, including OsGlyRS3 and OsSnRK1A, which are involved in flowering, sugar signaling, chloroplast development, and hormone metabolism. Structural modeling suggested that OsGlyRS3 may stabilize OsPRR37 protein complexes, while OsSnRK1A could modulate its DNA-binding capacity under sugar-deficient conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe findings establish \u003cem\u003eOsPRR37\u003c/em\u003e as a central regulatory hub that coordinates flowering, energy metabolism, chloroplast function, and stress adaptation through a hierarchical network comprising a Modulatory Layer of protein interactors, a Direct Target Layer of DNA-bound genes, an Indirect Coherent Layer of transcriptional cascades, and a Diffuse Response Layer encompassing broad metabolic outputs. This model provides a comprehensive framework for understanding how \u003cem\u003eOsPRR37\u003c/em\u003e integrates circadian signals to control multiple agronomic traits and offers valuable targets for breeding climate-resilient, high-yielding rice varieties.\u003c/p\u003e","manuscriptTitle":"A multi-layered regulatory model uncovers the central role of OsPRR37 in coordinating multiple agronomic traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 13:39:35","doi":"10.21203/rs.3.rs-7366051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T07:48:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T06:08:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163713767015126116654917939996163282970","date":"2025-09-19T03:39:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T00:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89512867633984888135260088827627311589","date":"2025-09-07T23:22:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-07T13:57:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-05T15:30:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T04:03:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T04:02:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-13T14:36:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99cded9c-3994-4910-8f88-1edb917b6939","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:03:53+00:00","versionOfRecord":{"articleIdentity":"rs-7366051","link":"https://doi.org/10.1186/s12870-025-08001-8","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-12-20 15:58:41","publishedOnDateReadable":"December 20th, 2025"},"versionCreatedAt":"2025-09-12 13:39:35","video":"","vorDoi":"10.1186/s12870-025-08001-8","vorDoiUrl":"https://doi.org/10.1186/s12870-025-08001-8","workflowStages":[]},"version":"v1","identity":"rs-7366051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7366051","identity":"rs-7366051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}