Small RNA sequencing unveils predominant expression patterns and miRNA-target modules active during seed development in sorghum

Small RNA sequencing unveils predominant expression patterns and miRNA-target modules active during seed development in sorghum | 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 Small RNA sequencing unveils predominant expression patterns and miRNA-target modules active during seed development in sorghum Rubi Jain, Garima Yadav, Namrata Dhaka, Ira Vashisht, Anisha Maheshwari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7092705/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sorghum is a prominent cereal crop of global importance. Advances in seed research are essential for the enhancement of seed yield and nutritional value in sorghum. Here, we report small RNA profiling from five stages of sorghum seed development depicting miRNA dynamics during pollination, fertilization, early seed development, grain filling, and maturation. We identified 226 miRNAs, 72 of which were known while 154 are novel. Based on the predominant expression patterns, all miRNAs could be classified into five distinct groups. Target prediction unveiled 6640 miRNA-target modules of which 1507 were predicted to regulate grain size. Based on the experimentally verified functions of the orthologs of miRNAs and their targets, 83 modules comprising 16 miRNA families and 24 target genes were shortlisted as high-priority candidates for grain size control. Among these, 13 modules co-localized with previously known grain size quantitative trait loci (QTLs) in sorghum. Furthermore, a total of 12, 5, 3, and 3 candidate modules were implicated in regulating starch content, seed dormancy, seed vigor, and seed shattering, respectively. By integrating the expression profiles of miRNAs and their targets with the comparative genomic data, we could gain global insights into the specific roles of miRNAs in regulating seed development and associated agronomic traits. Grain size miRNA sorghum seed development small RNA sequencing grain filling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The grains of cultivated sorghum ( Sorghum bicolor ) serve as a staple food for a large fraction of the global population. Many regions of Africa, Asia, and other continents utilize sorghum grains as a staple crop (Mundia et al. 2019 ). Besides, sorghum is also a prominent fodder and biofuel crop (Mathur et al. 2017 ; Khoddami et al. 2023 ). The mature sorghum grain comprises the seed coat, endosperm, and embryo. Like other cereals, the endosperm occupies the maximum portion of the mature grain. Nutritionally, sorghum seeds contain 55–76% carbohydrates, 7–15% proteins, ~ 3% lipids, and small quantities of vitamins, minerals, and phenolic compounds (Khoddami et al. 2023 ). Due to high dietary fiber, essential elements, and antioxidant content, sorghum seeds are also considered nutraceuticals (Tanwar et al. 2023 ). Overall, sorghum grains have a high economic value, with grain weight and size as major contributors to grain yield (Takanashi 2023 ). In addition, starch content, starch quality, seed shattering, etc. are also agriculturally important seed traits in sorghum. Grain size has been a target of artificial selection since domestication. However, further improvement in grain size through traditional breeding approaches is challenging (Liu et al. 2024 ). Hence, it is imperative to dissect the molecular basis of grain development and to identify candidates for genetic improvement in sorghum. MicroRNAs have been experimentally demonstrated to act as key modulators of seed traits (Dhaka and Sharma 2021 ). A recent study reported the role of miR159 in positively regulating grain size and weight in maize (Wang et al. 2023 ). Zma-miR159 regulates cell division and proliferation in endosperm cells by targeting MYB transcription factor genes, ZmMYB74 and ZmMYB138 . Overexpression of Zma-miR159 or loss of function of ZmMYB74 lead to increased grain size. Another recent study in maize dissected the role of miR169- ZmNF-YA13 (NUCLEAR FACTOR-Y SUBUNIT A13) module in determining seed size and weight (Zhang et al. 2022 ). Zma-miR169o regulates the number of endosperm cells and hence seed size by negatively regulating ZmNF-YA13 expression. Overexpression of zma-miR169o in maize led to enhanced seed size and weight (Zhang et al. 2022 ). These studies suggest that miRNAs play key role in regulating seed size and weight as top-tier regulators. Considering the significance of miRNAs in regulating seed size and agronomic attributes, small RNA transcriptome profiling has been extensively leveraged to identify miRNA target modules during seed development. miRNA expression profiling from developing seeds has been used to identify important candidates in various crops. For instance, miRNA profiling from 4 stages of seed development in maize highlighted important miRNA candidates associated with embryogenesis and nutrient storage (Li et al. 2016 ). Similarly in the oilseed crop B. juncea , miRNAs expressed during different seed stages have been used to shortlist candidates for seed size, seed coat color, and oil content (Jain et al. 2023 ). In chickpea, miRNA expression dynamics using small- and bold-seeded varieties revealed important candidates for seed weight (Khemka et al. 2021 ). Recently, miRNA profiling from embryos and endosperm collected at different maturation stages in rice provided miRNA-target modules associated with seed dormancy and their relationship with phytohormones including auxin, abscisic acid, and ethylene (Park et al. 2024 ). In sorghum, the role of miRNAs in regulating grain development and associated agronomic traits remains unexplored. Here, we address this lacuna and report identification, in silico characterization, and analysis of sorghum seed miRNAome. Further, we integrated the small RNA data with our recently published transcriptome data from developing sorghum grains to identify the most suitable miRNA-target modules for grain improvement (Jain et al. 2024 ). Mapping of orthologous gene functions and quantitative trait loci (QTLs) on these modules underlined high-priority candidates for engineering seed traits. Materials and methods Growing of plants and sample harvesting Sorghum bicolor variety M35-1 plants was grown under open field conditions at Jawaharlal Nehru University, New Delhi, India, from February to June 2021 in an open field. Mature pistils 1–2 days before pollination (1–2 DBP) and developing seeds at 0–2, 10, 20, and 30 days after pollination (DAP) were collected, as described in our previous study (Jain et al. 2024 ). The tissue samples were snap-frozen in liquid nitrogen and stored at -80°C until further use. RNA extraction and small RNA profiling Total RNA was extracted from all the samples using TRIzol™ (Ambion) reagent, as described previously (Jain et al. 2024 ). Genomic DNA was removed using Turbo DNA-free™ Kit (AM1907, Invitrogen) according to the manufacturer’s instructions. The concentration and quality of the purified RNA samples were determined using Nanodrop™ Spectrophotometer (Nanodrop™ 2000), agarose gel electrophoresis, Qubit fluorometer (Thermo Fisher Scientific, USA), and Agilent 2200 TapeStation. Two biological replicates of pistils collected at 1–2 DBP and 0–2 DAP, and three biological replicates of developing seeds collected at 10, 20, and 30 DAP stages, with RNA integrity number (RIN) values > 7, were used for small RNA library preparation using QIAseq® miRNA Library Kit (Qiagen, Maryland, U.S.A.), as per the manufacturer’s guidelines. A total of 13 small RNA libraries were prepared. Thereafter, 50 bp single-end small RNA sequencing was performed using the Illumina NovaSeq 6000 sequencing platform. Small RNA sequencing data analysis The quality of the small RNA sequencing data was checked using FastQC (Andrews 2010 ). Next, Cutadapt v.4.1 (Martin 2011 ) was used to remove the adapter sequences and to select the reads ranging from 20 to 24 nucleotides (nt) in length. For subsequent analysis, low-quality reads were removed using the fastq_quality_filter tool from the FASTX-toolkit ( http://hannonlab.cshl.edu/fastx_toolkit/commandline.html ), with a threshold of less than 70% of bases having a Phred score < 30. Subsequently, redundant reads were removed from each library by collapsing the reads using fastx_collapser from the FASTX-toolkit. Finally, the unique reads were utilized to identify miRNAs. Identification of miRNAs The unique reads were first screened against the Rfam database v.14.9 using the miRDeep-P2 program (Kuang et al. 2019 ). The reads mapping to housekeeping non-coding RNAs, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs) were removed. The remaining reads were mapped to the Sorghum bicolor reference genome v.5.1 ( https://phytozome-next.jgi.doe.gov/info/Sbicolor_v5_1 ). Finally, miRNAs were predicted using miRDeep-P2 as described in (Jain et al. 2023 ). The miRNAs were aligned against the miRBase database v.22.1 ( https://www.mirbase.org/ ) using Bowtie v.1.3.1 (Langmead 2010 ), allowing one mismatch. Those mapping to miRBase were considered as known miRNAs, while the remaining were predicted as novel miRNAs. To increase the stringency of prediction, only miRNAs present in at least two independent sRNA libraries were used for further analysis. For nomenclature, the known miRNAs were named with the prefix ‘sbi’ ( Sorghum bicolor ), followed by the miRNA family name. The different mature miRNA sequences, within the same miRNA family, were differentiated by adding a numeric suffix after the family name. For instance, sbi-miR156.1, sbi-miR156.2, and so on. On the other hand, the novel miRNAs were named from sbi-novel-miR1 to sbi-novel-miR154. The minimum free energy (MFE) and minimum free energy index (MFEI) of miRNA precursors were calculated as detailed in our previous study (Jain et al. 2023 ). The genomic location of miRNA precursors was determined using bedtools ( https://bedtools.readthedocs.io/en/latest/ ). Expression analysis of miRNAs The raw counts of unique miRNAs were normalized as reads per million (RPM) using the formula: Normalized expression = (Actual miRNA read count/Total count of clean reads)*10 6 Pairwise differential expression was carried out using the R package DESeq2 v.1.34.0 (Love et al. 2014 ). The miRNAs exhibiting log 2 (fold change) ≥1 or ≤-1 and FDR (false discovery rate) ≤0.05 were considered as differentially expressed. The miRNAs were classified into different groups based on stage-specific or peak expression as compared between different stages. The miRNAs expressed only in any particular stage, while exhibiting 0 RPM in the other stages were considered as stage-specific. The heatmaps for visualizing expression patterns were prepared using MultiExperiment Viewer (MeV) (Howe et al. 2010 ). Prediction of miRNA targets and construction of miRNA-mRNA networks To predict putative targets of miRNAs, we used a set of 31,337 genes retrieved from our transcriptomic dataset generated from sorghum M35-1 seeds at the same stages of development (Jain et al. 2024 ). Putative target genes were first predicted using the psRNATarget web server (Dai et al. 2018 ), using default parameters of schema v2 (2017 release). The top targets were limited to 50, and Sorghum bicolor transcripts (annotated in v3.1.1 Phytozome) were selected in the psRNATarget for target prediction. The identified targets were further shortlisted by mapping to our in-house transcriptome data. We had earlier collated information on pathways, transcription factors (TFs), orthologs, QTLs, and candidate genes for grain size and other grain traits in sorghum (Jain et al. 2024 ). This information was retrieved for the target genes for functional annotation analysis. Pathway and TF enrichment analysis was performed using a hypergeometric test, with a p-value cut-off of ≤0.05 and a false discovery rate (FDR) of ≤0.05. The transcriptome data of predicted targets was obtained from our previous study (Jain et al. 2024 ), and the correlation in expression of small RNAs and corresponding targets was assessed in Microsoft Excel. Inversely correlated targets were identified using the Pearson correlation coefficient cut-off ≤-0.7. The miRNA-target networks of selected modules were visualized using Cytoscape (v.3.9.1) (Shannon et al. 2003 ). Results and Discussion Identification and characterization of miRNAs from pistils and developing seeds of Sorghum bicolor We had previously investigated transcriptional dynamics of pistils collected 1–2 days before pollination (DBP) and developing seeds at four different stages viz., 0–2 days after pollination (DAP), 10 DAP, 20 DAP, and 30 DAP in Sorghum bicolor M35-1 (Jain et al. 2024 ). These stages corresponded to pre-pollination pistils (1–2 DBP), pollination, fertilization and early embryogenesis (0–2 DAP), soft dough (10 DAP), hard dough (20 DAP), and physiological maturity (30 DAP) stages of seed development (Jain et al. 2024 ). In this study, we performed small RNA sequencing from these five stages and identified the miRNAs and miRNA-target modules associated with seed development. A total of 13 libraries were generated. The Pearson’s correlation coefficient between biological replicates for all libraries ranged from 0.7 to 0.9, except for one library (30 DAP_3), which exhibited low correlation and hence, was removed from further analysis (Supplementary Fig. S1 a). The remaining 12 libraries yielded > 175 million raw reads (Supplementary Table 1). After adaptor trimming, length filtering (20–24 nt), and removal of low-quality reads, ~ 86 million high-quality reads were retained. These were collapsed to 19.58 million unique reads (Supplementary Table 1). The length distribution of unique sRNA reads across all stages peaked at 24-nt (Supplementary Fig. S1 b), conforming to that reported in other plants (Meijer et al. 2022 ). For miRNA identification, the unique reads mapping to noncoding RNAs such as rRNAs, tRNAs, snRNAs, and snoRNAs were removed. The remaining reads were mapped to the sorghum genome, and the predicted miRNAs were further mapped to Viridiplantae mature miRNA sequences from the miRBase v.22.1. Additionally, only miRNAs present in at least two independent sRNA libraries were considered for further analysis. This analysis identified a total of 226 miRNAs, of which 72 were mapped to the known miRNAs, while the remaining 154 were designated as novel miRNAs (Supplementary Table 2). These miRNAs originated from 467 putative precursors, with 147 and 320 precursors for known and novel miRNAs, respectively (Supplementary Table 2). The precursor lengths for the known miRNAs ranged from 57 to 220 nt, with an average precursor length of 110 nt, while the precursor lengths for the novel miRNAs varied from 43 to 227 nt, with an average length of 114 nt (Supplementary Fig. S2 a). The miRNA precursors mapped to five genomic locations, including intergenic (59%), intronic (28%), CDS (6%), 5' UTR (3.6%), and 3' UTR (3%) regions with the highest number of miRNA precursors originating from intergenic regions (Supplementary Fig. S2 b). The GC content in known and novel miRNA precursors varied from 24.6 to 71.6% and 18.3 to 84.1%, respectively (Supplementary Fig. S2 c). The negative Minimum free energy Index (MFEI) for the known miRNA precursors ranged from 0.7 to 3.3 Kcal with an average MFEI value of -2 Kcal, while the negative MFEI value for the novel miRNA precursors varied from 0.5 to 6.8 Kcal, with an average of -2 Kcal (Supplementary Fig. S2 d). The length distribution of miRNAs showed that both known and novel miRNAs ranged from 20 to 24 nt in length, with the highest frequency of 21 nt miRNAs (Fig. 1 a). The base distribution of the mature miRNA sequence revealed ‘U’ as the most frequently occurring first base for both known and novel miRNAs (Fig. 1 b). These findings are consistent with those reported in other plant species (Xue et al. 2009 ; Curaba et al. 2012 ; Kang et al. 2012 ; Han et al. 2014 ). A total of 33 known miRNA families were detected in our data. Among these, miR156, miR169, miR171, and miR172 were the largest with 5 members each, followed by miR164, miR166, miR167, and miR396 families with 4 members each. The miRNA families miR399, and miR408 had 3 members each while miR159, miR160, miR393, miR395, miR398, miR2118, and miR5564 had 2 members each. The remaining miRNA families including miR168, miR319, miR384, miR390, miR394, miR397, miR479, miR528, miR529, miR530, miR827, miR1432, miR5169, miR5385, miR6223, and miR6234 had single member each (Fig. 1 c). Notably, the high number of isoforms of miR156, miR169, miR171, and miR172 families has been reported in developing seeds of other crops as well (Li et al. 2021a ). The chromosomal distribution of miRNAs was uneven, with chromosome 02 harboring the maximum number of total (44) and known miRNAs (15). On the other hand, the maximum number of novel miRNAs was observed on chromosome number 1 (32) (Fig. 1 d). Overall, the sequence features and distribution pattern of sorghum miRNAs were consistent with those reported in other crop species. Integrated analysis with transcriptome for target identification and functional annotation analysis We used the transcriptome data generated from the same stages of seed development in our earlier study (Jain et al. 2024 ) to predict the miRNA targets using the psRNAtarget server. A total of 4504 target genes and 6640 miRNA-target modules were identified. Targets were identified for all 226 miRNAs, with the number of targets for each miRNA ranging from 17 to 40 (Supplementary Table 3). Based on the ortholog information, literature analysis, pathway mapping, and TF information of sorghum genes previously compiled for our recent transcriptome study (Jain et al. 2024 ), we assigned functional categories to all the target genes. Among the 4504 targets, rice and Arabidopsis orthologs were identified for 3206 and 2414 genes, respectively (Supplementary Table 3). Further, 2511 genes could be assigned MapMan pathways, and 307 encoded transcription factors (Supplementary Table 3). The functional annotation analysis based on cross-species comparison implicated 32 target genes in regulating grain size and another 10, in modulating starch content, 3 in seed vigor, and 1 in both seed dormancy and seed shattering (Supplementary Tables 6 & 7). Magnitude and distribution of miRNA expression during seed development in sorghum Analysis of expression patterns of the miRNAs in all five temporal stages revealed 42 (19%) of the 226 miRNAs as common to all five stages, whereas 15 (6.6%), 41 (18%), and 80 (35.3%) miRNAs were only detected in any four, three, and two stages, respectively (Fig. 2 a, Supplementary Table 2). Further, 48 (21.2%) miRNAs were stage-specific (Fig. 2 a, Supplementary Table 2). The stage-wise expression dynamics showed that a larger proportion of miRNAs exhibited high expression values > 1000 RPM in the 1–2 DBP and 0–2 DAP stages, while the 10, 20, and 30 DAP stages instead had a higher proportion of low expression miRNAs with expression values ranging from 10–100 RPM (Supplementary Table 2, Fig. 2 b). A total of 15 miRNAs with an average RPM > 10,000 were identified as top expressed miRNAs in our data (Fig. 2 c). Of these, 12 were known, while 3 were novel. Notably, among the known miRNAs, there were 3 family members from miR166, 2 family members each from miR5564, miR167, and miR171, and one member each from miR168, miR156, and miR319. The novel miRNAs were sbi-novel-miR98, sbi-novel-miR144, and sbi-novel-miR17 (Fig. 2 c, Supplementary Table 2). Several of these high-expressing miRNAs in developing sorghum seeds have established roles in seed development. For example, miR156, miR166, miR167, and miR168 regulate embryo development (Rodrigues and Miguel 2017 ). They are also reported to express highly during seed development in other crop plants (Bai et al. 2017 ). However, miR5564, the miRNA with the highest expression in our data has not been reported to express in developing seeds of any other plant species. It has earlier been reported to exhibit high expression in developing sorghum anthers (Dhaka et al. 2020 ) and leaves (Puli et al. 2021 ). It showed very high expression in our data, with maximum accumulation at seed maturity. Moreover, most of the targets of miR5564 code for PPR (Pentatricopeptide repeat) proteins. In Arabidopsis , several PPR proteins have been reported to regulate embryogenesis (Cushing et al. 2005 ). Thus, considering evidence of PPR-mediated regulation of embryogenesis, and detection of miR5564 in sorghum seeds makes miR5564 an interesting candidate for further exploration. Apart from miR5564, the highly abundant novel miRNAs sbi-novel-miR98, sbi-novel-miR144, and sbi-novel-miR17 are also important candidates for further investigation, as their high expression is suggestive of their importance for seed development. Differential expression of miRNAs Differential expression analysis at each stage with respect to the preceding stage obtained a total of 102 differentially expressed miRNAs exhibiting log 2 fold change ≤-1 or ≥ 1 (FDR < 0.05) (Supplementary Table 4). Among these, 15, 64, 15, and 48 miRNAs were differentially expressed in the comparison sets of 0–2 DAP vs 1–2 DBP, 10 DAP vs 0–2 DAP, 20 DAP vs 10 DAP, and 30 DAP vs 20 DAP, respectively (Fig. 3 a). None of the miRNAs were common to all the comparison sets, while 36 miRNAs were detected in more than one comparison set. In the 0–2 DAP vs 1–2 DBP comparison, 11 miRNAs were upregulated, while 4 miRNAs were downregulated. In the 10 DAP vs 0–2 DAP comparison, 40 miRNAs were upregulated, while 24 were downregulated. In the 20 DAP vs 10 DAP stages, 8 and 7 miRNAs were upregulated and downregulated, respectively, while in the 30 DAP vs 20 DAP comparison, 19 and 29 miRNAs were upregulated and downregulated, respectively (Fig. 3 b). These results highlight the maximum difference in miRNA profiles during 0–2 DAP to 10 DAP transition and then 20 DAP to 30 DAP transition. This is consistent with what we observed in our transcriptomic study from these stages (Jain et al. 2024 ). Further, an integrated expression analysis of miRNAs and their targets based on the expression profiles of the miRNAs and respective targets showed 678 miRNA-target modules (127 miRNAs and 596 targets) exhibiting strong inverse correlation (Pearson correlation coefficient ≤ -0.7) (Supplementary Table 3). Among these, 52 miRNAs and 187 targets, constituting 195 modules, were differentially expressed (Supplementary Fig. S3). This finding was not surprising, as multiple regulatory pathways can impact mRNA levels at a given time. Therefore, one may not observe an inverse correlation in miRNA-mRNA levels during developmental stages (Diener et al. 2024 ). Grouping of miRNAs based on expression patterns reveals candidate miRNAs and miRNA-target modules regulating grain traits Next, we further investigated the expression profiles to correlate the miRNA expression dynamics with functional annotations. First, we classified all the miRNAs into five groups based on the respective stages in which they exhibited specific or peak expression (Fig. 4 ). Next, we integrated information obtained from literature and public databases for cross-species orthologs analysis and QTL mapping to obtain functional insights. The biological relevance of these distinct patterns was inferred based on the experimentally verified functions assigned to the homologs of these miRNAs and their targets in other plant species. Interestingly, the majority of the known miRNAs also exhibited differential expression, indicating a significant shift in their expression in subsequent stages of seed development. Based on literature survey, a total of 19 known miRNA families have been implicated in the regulation of grain size so far. Our data contained a total of 52 miRNAs from these families. These miRNAs targeted 1011 genes, constituting a total of 1493 miRNA-target modules. Furthermore, we identified 14 novel miRNAs that target genes involved in the regulation of seed size, comprising 14 novel miRNA-target modules for seed size. Thus, 1507 miRNA-target modules were identified as candidates for seed size regulation (Supplementary Table 5). Of these 1507 modules, 83 candidate miRNA-target modules are those for which the entire module has been previously characterized for grain size regulation and hence were highlighted as high-priority candidates in each group (Supplementary Table 6, Fig. 5 , Supplementary Fig. S4). Besides grain size, 12 miRNAs and 10 target genes constituting 12 modules were identified as regulators of starch content, while 5 miRNAs, and 1 target gene constituting 5 modules regulating seed dormancy, 2 miRNAs, and 3 target genes constituting 3 modules regulating seed vigor, and 3 miRNAs, and 1 target gene constituting 3 modules regulating seed shattering were identified (Supplementary Table 7). These modules were also prioritized in each group for the regulation of seed traits. Group 1 miRNAs exhibit predominant expression in pre-pollination pistils Group 1 consisted of 36 miRNAs exhibiting predominant expression in pre-pollination pistils, as they were either 1–2 DBP-specific or exhibited peak expression at 1–2 DBP (Fig. 4 ). Among the known miRNA families, miR319, miR384, miR394, and miR6223 were exclusive to this group. Some of these miRNA families have been previously detected from reproductive tissues in other crop species as well. For example, miR394 exhibits high expression in floral buds with low expression in later seed stages in B. napus and has been associated with floral and seed development (Song et al. 2015 ). Similarly, miR171 has also been shown to accumulate in the reproductive organs, with high expression in young spikes and ovaries in barley (Curaba et al. 2013 ). One of the group 1-specific novel miRNAs, sbi-novel-miR49, exhibited very high expression (> 2210 RPM), and therefore, is an interesting candidate for further exploration. Functional annotation analysis of target genes of group 1 miRNAs revealed phytohormone action (auxin), carbohydrate metabolism, plant organogenesis, cell division, and RNA biosynthesis as enriched pathway categories (Fig. 6 a, Supplementary Table 8). miRNA-mediated regulation of auxin signaling during embryo development has previously been established in barley (Bai et al. 2017 ). Several TF families including MADS, HD-ZIP, GRF, GRAS, and NF-YA were enriched in this group (Fig. 6 b, Supplementary Table 9). In rice, MADS-box genes express abundantly in ovaries and early hours after fertilization, but their expression declines 48 hours after fertilization conforming with the pattern observed in this study (Paul et al. 2020 ). This indicates that miRNAs may be crucial factors in the downregulation of MADS-box genes during the transition from pre- to post-fertilization transition in cereals. Based on the candidate miRNA/target gene information, we identified 16 known and 1 novel miRNA and 366 corresponding targets, comprising 473 miRNA-target modules as candidates for grain size in this group (Supplementary Table 5). Among these, 24 were high-priority modules as both the miRNAs and the corresponding target genes have been characterized for regulation of grain size in other crops (Supplementary Table 6, Fig. 5 ). These modules consisted of known miRNAs of miR156, miR160, miR164, miR167, miR169, miR171, and miR396 families (Fig. 5 ). MiR156- SPL12 (SQUAMOSA PROMOTER-BINDING-PROTEIN LIKE 12) module has been earlier reported to regulate seed size in rice (Zhang et al. 2021 ). The perturbation of the miR160- ARF18 (AUXIN RESPONSE FACTOR 18) module also leads to modulation of grain size and starch content (Huang et al. 2016 ). MiR164- NAC module regulates expansin genes which control seed expansion in maize (Zheng et al. 2019 ). Another crucial module is miR167- ARF12 (AUXIN RESPONSE FACTOR 12) . In rice, this module has been shown to mediate auxin and brassinosteroid signaling for the regulation of cell division in the developing grains. miR167 acts downstream of miR159, and targets OsARF12 , which in turn activates OsCDKF (CYCLIN-DEPENDENT KINASE F) , a positive regulator of cell proliferation during grain filling. Thus, suppression of miR167, or overexpression of ARF12 leads to an increase in seed size in rice (Zhao et al. 2023 ). Similarly, miR169- NF-YA13 has been shown to regulate grain size in maize. miR169 targets NF-YA13 , which further positively regulates YUC1 (YUCCA 1) positively. YUC1 modulates auxin signaling and enhances cell division and expansion in developing grains (Zhang et al. 2022 ). Conversely, miR171- HAM (HAIRY MERISTEM) regulates grain weight and other traits in rice (Um et al. 2022 ). Of all these modules, miR396- GRF (GROWTH REGULATING FACTOR) is one of the most well-characterized modules for miRNA-mediated regulation of seed size in both monocots and dicots (Liebsch and Palatnik 2020 ) (Fig. 5 ). In rice, GRF4 positively regulates cell division positively during grain development. GRF4 is the causal gene for rice grain size QTL GS2 (GRAIN SIZE ON CHROMOSOME 2) , and a 2 bp mutation in its miR396-target region leads to heavier grains (Duan et al. 2015 ). Furthermore, the sbi-miR396.2 target in our data, GRF4/GS2 (GRAIN SIZE ON CHROMOSOME 2)/GL2 (GRAIN LENGTH 2)/PT2 (PANICLE TRAIT 2)/LGS1 (LARGE GRAIN SIZE 1) (Sobic.004G269900), colocalized with the grain size QTL QGWGT4.17 (Supplementary Table 10). Therefore, these modules can be effectively utilized for engineering grain size in sorghum. Besides, the module sbi-novel-miR121- ISA2 (ISOAMYLASE 2) (Sobic.009G127500) emerged as a candidate for grain starch content (Supplementary Table 7), as ISA2 is involved in starch synthesis in sorghum (Hashimoto et al. 2023 ). Group 2 miRNAs exhibit predominant expression in post-pollination ovaries Group 2 comprised 38 miRNAs with 24 known and 14 novel miRNAs that were either specifically expressed in both 1–2 DBP and 0–2 DAP stages, or 0–2 DAP-specific, or showed 0–2 DAP-peak (Fig. 4 ). Several miRNA families including miR172, miR395, miR397, miR399, miR529, miR1432, miR2118, miR5169, and miR5385 were exclusive to this group. Interestingly, all five members of the miR172 family were present in this group (Fig. 4 ). Their expression profile indicates that miR172 is important for floral and early seed development, but its expression declines in the subsequent seed stages. In barley, miR172 targets cly1 (CLEISTOGAMY 1) gene and regulates cleistogamy (Anwar et al. 2018 ). We also noted a very interesting parallel in the developing grains of maize (Li et al. 2016 ). A similar expression pattern was observed for three group 2 miRNAs viz., miR172, miR395, and miR529, with high expression in the early stage of maize seed development (6 DAP), and a decrease in expression after that. Therefore, these miRNAs may have conserved roles in regulating early embryogenesis during seed development. Further, miR2118 has earlier been reported to exhibit high expression in developing anthers and regulate male fertility in cereal crops (Song et al. 2012 ; Zhai et al. 2015 ). We had also observed high expression of this miRNA in developing sorghum anthers (Dhaka et al. 2020 ). However, nothing is known about its possible involvement in regulating female gametophyte development. Our data showed that both members of miR2118 exhibit significant expression in 1–2 DBP and 0–2 DAP stages. It would be interesting to explore their role in female gametophyte and early seed development. Another exclusively detected family of this group was miR5385 with sbi-miR5385.1 detected in 0–2 DAP stage only. Previously, this miRNA has been associated with drought stress at the seedling stage in sorghum (Katiyar et al. 2015 ). However, its role in the regulation of seed development is unknown. MiR5169 from this group has been earlier associated with floral and gametophyte development in Xanthoceras sorbifolium (Wang et al. 2019 ). It has also been shown to have possible involvement in the regulation of male fertility in wheat (Wang et al. 2018 ). Since this miRNA has not been previously characterized, and its targets are unknown, it is a good candidate for further characterization. The miRNAs preferentially expressed in this group were enriched for targets associated with RNA biosynthesis, plant reproduction, nutrient uptake, protein biosynthesis, RNA processing, etc., (Fig. 6 a, Supplementary Table 8). The TF families enriched in this group include ARR, C2H2, and AP2 (Fig. 6 b, Supplementary Table 9). Notably, AP2 TFs are major regulators of embryo and endosperm development (Ohto et al. 2009 ). Orthology and mapping analysis yielded 14 known and 2 novel miRNAs and 357 target genes, comprising 392 miRNA-target modules (Supplementary Table 5). Among these, 17 complete modules have characterized roles in regulating grain size (Supplementary Table 6, Fig. 5 ). These modules comprised several known miRNAs including miR159, miR167, miR171, miR172, miR396, miR408, miR529, and miR1432. MiR159, miR167, miR171, and miR396 are crucial seed size regulators, as described above. MiR172 targets SNB and Os06g43220 (IDS1; INDETERMINATE SPIKELET 1) in rice, and its overexpression leads to reduced seed weight apart from other floral and seed abnormalities (Zhu et al. 2009 ). miR172- SNB/SSH1 (SUPERNUMERARY BRACT/SUPPRESSION OF SHATTERING 1) module is also a proven regulator of seed shattering in rice (Jiang et al. 2019 ) and wheat (Debernardi et al. 2017 ). Notably, sbi-miR172.1 target in sorghum, SSH1 (Sobic.002G083600), colocalized with the grain size QTL QGWGT2.2 (Supplementary Table 10). The expression pattern of miR172 also corroborates with that reported in rice, as it was highly expressed in 1–2 DBP and 0–2 DAP stages in our data, but did not express in the seeds at 20 and 30 DAP stages. In rice also, it is highly expressed in developing panicles but showed low expression in 10 DAF grains (Zhu et al. 2009 ). The similar expression profile suggests a conserved function in sorghum as well. miR408 targets Plantacyanin and Laccase 13 in Arabidopsis and is a negative regulator of seed size (Song et al. 2018 ). miR529 regulates grain size, seed dormancy, and other agronomic traits in rice (Yan et al. 2021 ). Moreover, the miR1432- ACOT ( Acyl-CoA thioesterase) module has been very well established for its role in seed development in rice (Zhao et al. 2019 ). However, it was interesting to note that despite the high expression of miR1432 in our data we did not identify ACOT or any other related thioesterase protein as its putative target. However, we identified another module in our data, miR1432- EFH1 (EF-HAND FAMILY PROTEIN 1) , which is associated with maintaining the balance of grain yield and immunity (Li et al. 2021b ). Further, sbi-miR2118.2 was predicted to target Sh1 (SHRUNKEN 1) (Sobic.010G072300), suggesting its role in regulating starch content (Supplementary Table 7). Group 3 miRNAs exhibit predominant expression during early embryo and endosperm development This group comprised 36 miRNAs with predominant expression in the 10 DAP stage. These were 0–2 DAP and 10 DAP-specific or 10 DAP-specific, or exhibited 10 DAP-peak. The highest-expressing miRNAs in this group were sbi-miR171.3 and sbi-novel-miR98. They exhibited little or no expression in the 1–2 DBP and 0–2 DAP stages, and the highest expression in 10 DAP, which declined in subsequent stages (Fig. 4 ). This pattern suggests their role in the regulation of early seed development. In group 3, none of the known miRNA families were detected exclusively, but the miRNAs miR159, miR169, and miR171 families, represented here, have well-known roles in embryo and endosperm development (Zhao et al. 2018 ; Takanashi et al. 2018 ; Zhang et al. 2022 ). miR159.2 is a good candidate for the regulation of grain filling in this group with predominant expression in the 10 DAP stage. It has earlier been reported to express highly in rice and tartary buckwheat seeds as well (Li et al. 2021a ). Its role in endosperm proliferation has been experimentally shown in Arabidopsis (Zhao et al. 2018 ). The mRNA targets of miRNAs of this group have been associated with DNA damage response, protein translocation, cell wall organization, etc., (Fig. 6 a, Supplementary Table 8) and with significant representation of the CPP TF family (Fig. 6 b, Supplementary Table 9). During endosperm development, protein translocation plays an important role in determining grain quality (Roustan et al. 2020 ). Hence, these targets may be involved in the regulation of endosperm reorganization during seed development in sorghum. We also identified 3 known and 3 novel miRNAs and 94 corresponding target genes, comprising 94 miRNA-target modules as candidates for grain size (Supplementary Table 5). Of these, 6 modules are the high-priority modules for the regulation of grain size (Supplementary Table 6, Supplementary Fig. 4). These comprised miR159, miR169, and miR171. Notably, two targets for these miRNAs, sbi-miR169.1- DA1 (Sobic.010G064600) and sbi-miR171.3- NF-YB1 (NUCLEAR FACTOR-Y SUBUNIT B1) (Sobic.008G064100), colocalize with grain size QTL (Supplementary Table 10). Additionally, sbi-novel-miR129- SBEIIa (STARCH BRANCHING ENZYME IIa) (Sobic.006G066800) was identified as a potential regulator of grain starch content. (Supplementary Table 7). Group 4 miRNAs exhibit predominant expression during grain-filling stages This was the largest group with 66 miRNAs. These miRNAs were 10 DAP and 20 DAP-specific, or 20 DAP-specific, or showed peak expression at 20 DAP. This group harbored five critical miRNA families, miR168, miR398, miR528, miR530, and miR6234, as specific to this group (Fig. 4 ). Some of these candidates are the signatures of grain filling. For example, miR398 and miR528 have been implicated in regulating seed filling in rice as well (Peng et al. 2014 ). miR530 is highly expressed in rice embryos and positively regulates cell division and expansion in the developing seeds (Sun et al. 2020 ). The targets for this group were overrepresented from key pathway categories like protein homeostasis, solute transport, cell wall, secondary metabolism, etc., (Fig. 6 a, Supplementary Table 8), suggesting that miRNAs of this group may be crucial for regulating metabolic and transport pathways during grain filling. The HSF, bZIP, and NAC TF families also were enriched in this group (Fig. 6 b, Supplementary Table 9). miR164- and miR167-mediated targeting of NAC TFs has also been demonstrated during grain filling in rice (Peng et al. 2014 ). The grain size candidate identification highlighted 10 known and 5 novel miRNAs and 295 targets, constituting 296 miRNA-target modules (Supplementary Table 5). Of these, 16 are the high-priority modules (Supplementary Table 6, Fig. 5 ), with known miRNAs from miR156, miR164, miR167, miR168, miR398, and miR530 families. MiR168 regulates AGO18 (ARGONAUTE 18) and AGO1 , which further regulate grain size regulatory miRNAs, miR396 and miR529. Further, miR168- AGO module regulates many other agronomic traits directly and indirectly (Zhou et al. 2022 ). MiR168 exhibited high expression in both 10 DAP and 20 DAP stages, with a peak at 20 DAP, and sbi-miR168.1 targets Sobic.010G276600, an ortholog of rice OsAGO1d indicating a conserved pathway in sorghum as well. Further, miR398 targets CSD1-2 (Cu/Zn Superoxide Dismutases1-2) and CCS (copper chaperone of CSD) in rice, and overexpression of miR398 causes an increase in grain size (Lu et al. 2022 ). Similarly, miR530- PL3 (PLUS3 DOMAIN-CONTAINING PROTEIN) module acts downstream of PIL15 (PHYTOCHROME-INTERACTING FACTOR-LIKE 15) to regulate grain size. Overexpression of miR530 and knockdown of PL3 both lead to a decrease in grain size (Sun et al. 2020 ). Notably, the modules sbi-miR530.1- GS9 (GRAIN SIZE ON CHROMOSOME 9) (Sobic.002G220700), sbi-novel-miR20- glHAT1 (GNAT-LIKE HISTONE ACETYLTRANSFERASE)/GW6a (GRAIN WEIGHT ON CHROMOSOME 6a) (Sobic.010G210100), and sbi-novel-miR99- WRKY53 (Sobic.009G100500) consisted of genes colocalizing with grain size QTLs (Supplementary Table 10). Further, we determined 12 miRNA-target modules as candidates for regulating other grain traits in sorghum, with 7 and 3 modules involved in the regulation of grain starch content and seed vigor, respectively. (Supplementary Table 7). Interestingly, one of these, miR160, which putatively targets starch metabolism gene SSIIIb (SOLUBLE STARCH SYNTHASE IIIb) in our data, has also been identified as a candidate miRNA regulating starch synthesis in foxtail millet in a recent study (Li et al. 2024 ). miR168- AGO1 module is also experimentally determined to be a critical regulator of seed vigor in rice (Zhou et al. 2020 ). MiR398- CSD1 is also involved in the regulation of seed vigor in response to copper sulphate stress (Lu et al. 2022 ). Thus, miRNAs of this group are likely very important for regulating seed size, starch content, and vigor. Group 5 miRNAs exhibit predominant expression during seed maturation This group contained 50 miRNAs with predominant expression in the later stages of seed development as they were 20 DAP and 30 DAP-specific, or 30 DAP-specific, or exhibited 30 DAP-peak (Fig. 4 ). The miRNA families exclusive to this group were miR390, miR479, miR827, and miR5564. In B. napus also, miR390 was more prevalent in the mature seeds as compared to the developing seeds (Koerbes et al. 2012 ). Recently, miR390 has been shown to be a possible regulator of seed dormancy in rice (Park et al. 2024 ). The expression of miR390 in rice also increases with seed maturity. It has also been identified as a candidate for regulating seed germination and seed storability in maize (Song et al. 2022 ). This suggests that these known miRNA families may be important for the regulation of seed maturation and desiccation-related processes. For the miRNAs peaking at the 30 DAP stage, the targets showed enrichment of enzyme classification, lipid metabolism, protein modification, etc., (Fig. 6 a, Supplementary Table 8) and TFs of MYBs and SBP families (Fig. 6 b, Supplementary Table 9). Several SPL members are known to regulate seed dormancy (Miao et al. 2019 ; Qin et al. 2020 ), suggesting that the targets of the miRNAs of group 5 may be associated with late seed development. The candidate identification highlighted 9 known and 3 novel miRNAs with 236 targets, comprising 252 miRNA-target modules associated with grain size (Supplementary Table 5). Among these, 20 modules are high-priority modules. These comprised miRNAs from miR156, miR164, miR167, miR169, miR171, miR408, and miR827 families (Supplementary Table 6, Fig. 5 ). miR827- SPX-MFS1 (SYG1/PHO81/XPR1- MAJOR FACILITY SUPERFAMILY 1) module has recently been associated with grain weight and other traits in rice (Chen et al. 2024 ). Further, 5 of the targets ( SPL18, SPL16 /qGW8; GRAIN WIDTH 8, SPL14, ARF12, RGA1; RICE G PROTEIN ALPHA SUBUNIT 1/D1; DWARF 1 , and RGB1; RICE G PROTEIN BETA SUBUNIT 1 ) overlapped with grain size QTL regions (Supplementary Table 10). Further, two modules, sbi-miR827.1- Sh1 (Sobic.010G072300) and sbi-novel-miR45- PHO1 (STARCH PHOSPHORYLASE 1) (Sobic.001G083900) are candidates for grain starch content regulation as these target genes are known to be involved in starch biosynthesis in cereal grains (Chourey and Nelson 1976 ; Satoh et al. 2008 ). Interestingly, miR827 has recently been experimentally validated for the regulation of starch content in rice. STTM827 and Crispr-827 lines exhibit lower starch content and altered sugar quality in rice seeds (Chen et al. 2024 ). In our data, Sh1 (Sobic.010G072300), which is a major regulator of sucrose biosynthesis and metabolism (Chourey and Nelson 1976 ), is a putative target of miR827. Furthermore, the module sbi-miR156.1- SPL14/IPA1 (IDEAL PLANT ARCHITECTURE 1) (Sobic.007G210200) is a candidate for the regulation of seed dormancy (Supplementary Table 7). In rice, the miR156- IPA1 module regulates seed dormancy by controlling GA signaling (Miao et al. 2019 ). Hence, it is an important module for engineering pre-harvest sprouting in sorghum. It was also interesting to note that several members of the same miRNA family were assigned to different groups due to varied expression patterns. For example, miR156 and miR164 family members belonged to groups 1, 4, and 5. A similar finding has been previously reported in maize seeds, where Jin et al., ( 2015 ) showed that different members of the same miRNA family may exhibit stage- or spatially-specific expression within the seeds. Construction of miRNA-target networks with shortlisted modules reveals common genes targeted by multiple miRNAs To further elaborate the interrelationships between miRNA and targets regulating grain size, we constructed a miRNA-gene network regulating grain size in sorghum. We developed a miRNA-gene network by using all the 1507 modules identified in this study (Supplementary Table 5). Using the information of interconnected nodes through shared miRNAs and targets, we identified 1479 nodes and 2958 edges. Interestingly, we obtained a large network that showed interconnections between 19 miRNA families and several novel miRNAs (Fig. 7 ). Of these, miR394 showed the higher number of connections through shared targets. It had targets shared with miR167, miR172, miR164, miR396, and miR1432. Other families including miR172, miR396, and miR408 were also connected with multiple miRNA families (Fig. 7 , Supplementary Table 11). Novel interplays were noticed using miRNA-gene network analysis. For example, sbi-novel-miR50 targeted the miR396-target GRF4 , and sbi-novel-miR97 targeted the miR172 target SNB . We also found a total of 70 targets which were shared by more than one miRNA. Interestingly, miR156 and miR529 have nine shared targets. All of these were SPL genes, including the seed size regulators SPL14, SPL16, SPL12, SPL18 , and other SPLs (Sobic.006G171000, Sobic.003G406600, Sobic.004G058900, Sobic.002G247800, and Sobic.005G120600) (Supplementary Table 11). miR156 and miR529 are well-known combinatorial regulators that target the same genes. They share sequence similarity and hence target the same genes to regulate seed development (Li et al. 2023 ). This finding suggests that the identified miRNAs that possess shared targets likely function together to regulate sorghum seed development, allowing for more robust and complex regulation of gene expression. Conclusion We identified and characterized sorghum seed miRNAome using five different developmental stages. The expression profiling and stage-wise grouping highlighted the miRNAs crucial for floral and female gametophyte development, early embryogenesis, embryo and endosperm development, grain filling, and seed maturation (Fig. 8 ). The expression profiles showed that 22 miRNA families exhibited distinct group-wise profiles suggesting specific functions in each stage. The known functions of these exclusive families enabled the delineation of candidates for seed development. For instance, miR394 is associated with pistil development, miR172, miR295, and miR529 regulate early seed development, miR168, miR398, miR528, and miR530 are candidates for grain filling, while miR390, miR827, and miR5564 likely regulate seed maturation (Fig. 8 ). Target identification, functional annotation analysis, and integrated analysis with transcriptome data further highlighted the pathways, TFs, and interplay of miRNA and target expressions during sorghum seed development. Moreover, 19 known miRNA families, 66 miRNAs, and 1019 targets comprising 1507 miRNA-target modules emerged as candidates based on the orthologs. Of these, 83 modules were selected as high-priority candidates as they have been experimentally validated for their role in seed development in other crops (Fig. 5 , 8 ). Among these, miR156- SPL14/16/18 , miR167- ARF12 , miR396- GRF4/GS2/GL2 , and miR172- SSH1 emerged as the most important as they also overlapped with sorghum grain size QTLs. Among the novel miRNAs, sbi-novel-miR20, 45, 49, 98, 99, 121, and 129 were identified as important candidates based on their expression patterns and target analyses. Apart from an expansive investigation of grain size, we also obtained 23 miRNA-target candidates for grain starch content and other agronomic traits. These modules are suitable for functional validations using knockout and overexpression of miRNAs, and target mimic analysis. The allelic variation in targets may also be investigated in the sorghum germplasm to correlate with trait variation. Some of these modules are also involved in regulating other aspects of plant growth and development, and therefore, specific strategies may be employed to manipulate seed size specifically when targeting these modules. Alternatively, instead of the manipulation of miRNA, target genes may be edited to yield desired goals. The in-depth characterization of shortlisted miRNA-mRNA modules will be required to design informed strategies for effective engineering of seed traits in sorghum. Declarations Competing interests The authors declare no competing interests. Funding This work was supported by the Science and Engineering Research Board (SERB), and Department of Science & Technology (DST), Government of India, grant/award numbers: CRG/2019/001695; IFA17-LSP90; PDF/2019/002365; CRG/2020/003466; STR/2022/000013 and Indo‐German Science & Technology Centre (IGSTC), WISER grant/award number: IGST/WISER2023/RS/39/2023‐24/771). Author Contribution Conceptualization and designing of the study: N.D., R.A.S., Formal analysis: N.D., G.Y., R.J., I.V., A.T.; Funding acquisition: N.D., I.V., R.A.S.; Methodology: R.J., G.Y., I.V., N.D.; Supervision: N.D., M.K.S., R.A.S.; Visualization: N.D., G.Y., R.J., A.M., A.T.; Writing – original draft: N.D., R.A.S.; Writing – review & editing: N.D., R.A.S., G.Y., R.J., A.T., M.K.S., I.V. All authors read and approved the final manuscript. Acknowledgements ND gratefully acknowledges the research grant from the Science and Engineering Research Board (SERB): (CRG/2019/001695) and DST INSPIRE grant IFA17-LSP90 by the Government of India. RAS thanks DST-SERB for CRG/2020/003466 and STR/2022/000013 grants, and Indo-German Science & Technology Centre (IGSTC) for the WISER grant, IGST/WISER2023/RS/39/2023-24/771. RJ thanks financial assistance through an ICMR for the senior research fellowship. GY acknowledges CSIR-UGC for the senior research fellowship (HR0402430044). IV acknowledges the NPDF grant from the SERB, Government of India (PDF/2019/002365). AM thanks the Central University of Haryana, Mahendergarh, for the University Non-NET fellowship. AT thanks DST INSPIRE SHE for the scholarship. The authors are also thankful for the facilities provided by Jawaharlal Nehru University, New Delhi, DST FIST lab (SR/FST/LS-1-2019-471/C), and CIC Central University of Haryana, Mahendergarh. Data Availability The sequencing data has been deposited to the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) under the BioProject ID: PRJNA1224756. References Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ . Accessed on 1 May 2024 Anwar N, Ohta M, Yazawa T et al (2018) miR172 downregulates the translation of cleistogamy 1 in barley. 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Forests 10:1141. https://doi.org/10.3390/f10121141 Xue L-J, Zhang J-J, Xue H-W (2009) Characterization and expression profiles of miRNAs in rice seeds. Nucleic Acids Res 37:916–930. https://doi.org/10.1093/nar/gkn998 Yan Y, Wei M, Li Y et al (2021) MiR529a controls plant height, tiller number, panicle architecture and grain size by regulating SPL target genes in rice (O ryza sativa L). Plant Sci 302:110728. https://doi.org/10.1016/j.plantsci.2020.110728 Zhai J, Zhang H, Arikit S et al (2015) Spatiotemporally dynamic, cell-type–dependent premeiotic and meiotic phasiRNAs in maize anthers. Proc Natl Acad Sci USA 112:3146–3151. https://doi.org/10.1073/pnas.1418918112 Zhang M, Zheng H, Jin L et al (2022) miR169o and ZmNF-YA13 act in concert to coordinate the expression of ZmYUC1 that determines seed size and weight in maize kernels. New Phytol 235:2270–2284. https://doi.org/10.1111/nph.18317 Zhang X-F, Yang C-Y, Lin H-X et al (2021) Rice SPL12 coevolved with GW5 to determine grain shape. Sci Bull 66:2353–2357. https://doi.org/10.1016/j.scib.2021.05.005 Zhao Y, Peng T, Sun H et al (2019) miR1432- OsACOT (Acyl‐CoA thioesterase) module determines grain yield via enhancing grain filling rate in rice. Plant Biotechnol J 17:712–723. https://doi.org/10.1111/pbi.13009 Zhao Y, Wang S, Wu W et al (2018) Clearance of maternal barriers by paternal miR159 to initiate endosperm nuclear division in Arabidopsis. Nat Commun 9:5011. https://doi.org/10.1038/s41467-018-07429-x Zhao Y, Zhang X, Cheng Y et al (2023) The miR167-OsARF12 module regulates rice grain filling and grain size downstream of miR159. Plant Commun 4:100604. https://doi.org/10.1016/j.xplc.2023.100604 Zheng L, Zhang X, Zhang H et al (2019) The miR164-dependent regulatory pathway in developing maize seed. Mol Genet Genomics 294:501–517. https://doi.org/10.1007/s00438-018-1524-4 Zhou J, Zhang R, Jia X et al (2022) CRISPR-Cas9 mediated OsMIR168a knockout reveals its pleiotropy in rice. Plant Biotechnol J 20:310–322. https://doi.org/10.1111/pbi.13713 Zhou Y, Zhou S, Wang L et al (2020) miR164c and miR168a regulate seed vigor in rice. JIPB 62:470–486. https://doi.org/10.1111/jipb.12792 Zhu Q-H, Upadhyaya NM, Gubler F, Helliwell CA (2009) Over-expression of miR172 causes loss of spikelet determinacy and floral organ abnormalities in rice ( Oryza sativa ). BMC Plant Biol 9:149. https://doi.org/10.1186/1471-2229-9-149 Additional Declarations No competing interests reported. Supplementary Files Jainetal26052025SuppTables.xlsx Jainetal2025SuppFigures260525.pptx Supplementary Fig. S1 (a) Correlation plot showing the Pearson correlation coefficients between biological replicates of all five developmental stages. The scale on the left side represents range of correlation coefficients (from -1 to 1). Blue color represents a positive correlation while red color represents a negative correlation. (b) Length-wise distribution of miRNAs in different stages Supplementary Fig. S2 Characterization of miRNA precursors. (a) Length distribution of known and novel miRNA precursors. (b) Distribution of known and novel miRNAs precursors based on genomic locations. (c) GC content (%) distribution of known and novel miRNA precursors. (d) Graph representing negative minimum free energy index (MFEI) in Kilo-calories (Kcal) of known and novel miRNAs precursors Supplementary Fig. S3 Heatmap showing differentially expressed miRNAs and their inversely correlated targets that show differential expression. For each miRNA, only one corresponding target with the highest inverse correlation is shown. The color scale and values in the boxes represent log 2 transformed RPM values for miRNAs and TPM values for their respective targets at each developmental stage Supplementary Fig. S4 Heatmap showing the 83 miRNA-target gene modules involved in the regulation of seed size. Modules exhibiting inverse expression correlation between miRNA and their corresponding target are shown with an asterisk. Respective groups are also shown on the left. Expression values for both miRNAs (RPM) and targets (TPM) are transformed as log 2 (RPM +0.1) and log 2 (TPM +0.1), respectively Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7092705","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487658415,"identity":"e62539a3-e2cf-46bd-b603-ee249f301979","order_by":0,"name":"Rubi Jain","email":"","orcid":"","institution":"Jawaharlal Nehru University","correspondingAuthor":false,"prefix":"","firstName":"Rubi","middleName":"","lastName":"Jain","suffix":""},{"id":487658416,"identity":"21ec16f3-283e-4f2c-afc5-c657bc0434b1","order_by":1,"name":"Garima Yadav","email":"","orcid":"","institution":"Central University of Haryana","correspondingAuthor":false,"prefix":"","firstName":"Garima","middleName":"","lastName":"Yadav","suffix":""},{"id":487658417,"identity":"eeedead9-7e8b-48ce-9748-b30f1ac52363","order_by":2,"name":"Namrata Dhaka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBADxg0MbAwMH4AsNnaiNCRAtDDOAGlhJkULMw+IQ0iLOfvptA8/f9jJbmc/lvjZ5tc2eT5mBsYPH3Nwa7Hsyd08sych2XhnT9ph6dy+24ZtzAzMkjO34dZicCB3MwNPAnPihgPpDdK5PbcZgVrYmHnxaTn/djPjn4T6xA3nnzf/tuy5bU9Yy43czcw8CYcTN9xIOybN8ON2IhFa3m5mlkk7brzhxrM0y96G28ltzIzN+P1yPncz4xubatkN59OMb/z4c9t2fnvzwQ8f8WhBBYxtYLKBWPUg8IcUxaNgFIyCUTBSAABEJlZgmq/DiAAAAABJRU5ErkJggg==","orcid":"","institution":"Central University of Haryana","correspondingAuthor":true,"prefix":"","firstName":"Namrata","middleName":"","lastName":"Dhaka","suffix":""},{"id":487658418,"identity":"5a542ea9-d49a-4e37-a4dc-f7bbdf46db21","order_by":3,"name":"Ira Vashisht","email":"","orcid":"","institution":"Jawaharlal Nehru University","correspondingAuthor":false,"prefix":"","firstName":"Ira","middleName":"","lastName":"Vashisht","suffix":""},{"id":487658419,"identity":"38e798a2-9c81-42a9-bb72-4d00999e34fd","order_by":4,"name":"Anisha Maheshwari","email":"","orcid":"","institution":"Central University of Haryana","correspondingAuthor":false,"prefix":"","firstName":"Anisha","middleName":"","lastName":"Maheshwari","suffix":""},{"id":487658420,"identity":"8fdc3d58-f4ca-4c40-9285-d87736b7b1ed","order_by":5,"name":"Anisha Thalor","email":"","orcid":"","institution":"Central University of Haryana","correspondingAuthor":false,"prefix":"","firstName":"Anisha","middleName":"","lastName":"Thalor","suffix":""},{"id":487658421,"identity":"c38cc70c-ee43-4c91-b9fb-e71080c7e1cc","order_by":6,"name":"Manoj K. Sharma","email":"","orcid":"","institution":"Jawaharlal Nehru University","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"K.","lastName":"Sharma","suffix":""},{"id":487658422,"identity":"f09a1bad-f213-40be-87e7-baa05ac46375","order_by":7,"name":"Rita A. Sharma","email":"","orcid":"","institution":"BRIC-National Agri-Food \u0026 Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Rita","middleName":"A.","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-07-10 11:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7092705/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7092705/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87179833,"identity":"d1bd7707-9428-450f-8a16-d2be7318f5e6","added_by":"auto","created_at":"2025-07-21 09:32:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and characterization of miRNAs from pistils and developing seeds of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSorghum bicolor.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003e(a) Length distribution of known and novel miRNAs. The x-axis shows the length of miRNAs in base pairs, and the y-axis shows the number of miRNAs. (b) Base distribution of known and novel miRNAs. The x-axis shows the first base of miRNAs, and the y-axis shows the number of miRNAs. (c) Number of miRNA family members identified from the known miRNA families. (d) Chromosomal localization of known and novel miRNAs\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/39823ed7580afbd4b4eb7aa5.png"},{"id":87180703,"identity":"70144b46-c47c-42fe-a2eb-25244279bcbd","added_by":"auto","created_at":"2025-07-21 09:40:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":331579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression dynamics of miRNAs.\u003c/strong\u003e (a) Venn diagram showing the number of miRNAs expressed in 1-2 DBP, 0-2 DAP, 10 DAP, 20 DAP, and 30 DAP stages. (b) Bar graph showing the percentage of miRNAs based on the range of expression values in reads per million (RPM). (c) Bar graph showing the top 15 miRNAs with the highest amplitude of expression\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/5eb413d8a851c61d37eaaf84.png"},{"id":87179834,"identity":"57f25aba-1946-4bd0-b4c8-e191947d473b","added_by":"auto","created_at":"2025-07-21 09:32:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of miRNAs.\u003c/strong\u003e (a) Venn diagram showing a comparison of pairwise differential expression analysis across consecutive stages of seed development: 0–2 DAP \u003cem\u003evs\u003c/em\u003e 1–2 DBP, 10 DAP \u003cem\u003evs\u003c/em\u003e 0–2 DAP, 20 DAP \u003cem\u003evs\u003c/em\u003e 10 DAP, and 30 DAP \u003cem\u003evs\u003c/em\u003e20 DAP. (b) Number of differentially expressed miRNAs with respect to the preceding stage of seed development\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/73a1b842d15e68bce7291a19.png"},{"id":87180705,"identity":"fdfbe663-c2c7-4561-8428-d5043ca394db","added_by":"auto","created_at":"2025-07-21 09:40:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":733428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrouping of miRNAs based on stage-wise expression patterns using K-means clustering.\u003c/strong\u003e Heatmaps showing the groups of miRNAs based on the predominant expression at each developmental stage, represented by log\u003csub\u003e2\u003c/sub\u003e (RPM+0.1). Groups 1 to 5 are delineated based on the peak expression in distinct stages of seed development. Differentially expressed miRNAs are marked with an asterisk\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/ce0a1a71680731c9c32390a1.png"},{"id":87180713,"identity":"533c7f2b-3d84-4456-9312-1bc2ba28cbde","added_by":"auto","created_at":"2025-07-21 09:40:16","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh-priority miRNA-target modules for grain size regulation in sorghum\u003c/strong\u003e. A total of 83 high-priority modules comprising 16 miRNA families and 24 target genes are shown. The regulatory relationships between miRNAs and targets as inferred based on the experimentally characterized orthologs are shown. MiRNAs are shown in purple while target genes are shown in green. The positive and negative regulation are shown by arrows and blocked arrows, respectively. This Fig. was created using BioRender (https://www.biorender.com/)\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/014f515f128c05fe2087efc2.jpeg"},{"id":87179858,"identity":"9b299e45-fefe-44b8-945b-b38dd5ddbac8","added_by":"auto","created_at":"2025-07-21 09:32:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup-wise functional annotation analysis of targets of miRNAs. \u003c/strong\u003e(a) Dot plot showing group-wise pathway enrichment of target genes. The scale on the left represents MapMan pathway sub-categories while the scale on the right represents the p-value cut-off of £0.05 and number of genes in each category. (b) Dot plot showing group-wise transcription factor (TF) enrichment analysis of miRNA targets\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/943b6641c96b4b3598e79600.png"},{"id":87180707,"identity":"3ec2ee3d-671e-4923-982f-566308ccadb9","added_by":"auto","created_at":"2025-07-21 09:40:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":927927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork showing the interaction of miRNA-target modules implicated in controlling seed size. \u003c/strong\u003eOrange blocks highlight miRNAs, while purple and green blocks highlight their target genes. Gene names are shown for the genes regulating seed size, highlighted in green color\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/0f0c529652b35116484606ef.png"},{"id":87179840,"identity":"5daa4271-fa7a-4bbc-b650-774dee338ce6","added_by":"auto","created_at":"2025-07-21 09:32:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":621147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn overview of miRNA-mediated regulation of seed development and grain traits in sorghum.\u003c/strong\u003eThe pistil and seed stages are shown on the left. Groups 1 to 5 are shown in different colors and the predominant stage of expression is also highlighted using the respective colors. Details of the number of miRNAs, targets, exclusive miRNA families, enriched pathways, and transcription factor categories in each group are summarized. The total number of miRNA-target modules regulating seed traits in each group are given while the number of high-priority modules are mentioned in brackets. The number of candidate modules for other seed traits is also shown for all the groups\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/41b1b0a3b8bcaad3e50424de.png"},{"id":87183192,"identity":"1c5c0555-7e95-4f5e-a5c1-3eb3cc028626","added_by":"auto","created_at":"2025-07-21 10:04:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4346331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/882bb260-77a9-4087-82c3-4feea2ddbd3b.pdf"},{"id":87179839,"identity":"2d2b7cfa-fe13-4e51-8188-177585adc37c","added_by":"auto","created_at":"2025-07-21 09:32:10","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2590072,"visible":true,"origin":"","legend":"","description":"","filename":"Jainetal26052025SuppTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/f0ef32f9c243bab1a9d0d718.xlsx"},{"id":87180706,"identity":"35eee013-f5a9-4752-bf7b-3f021e429850","added_by":"auto","created_at":"2025-07-21 09:40:10","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3679260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S1\u003c/strong\u003e (a) Correlation plot showing the Pearson correlation coefficients between biological replicates of all five developmental stages. The scale on the left side represents range of correlation coefficients (from -1 to 1). Blue color represents a positive correlation while red color represents a negative correlation. (b) Length-wise distribution of miRNAs in different stages\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Fig. S2\u003c/strong\u003e Characterization of miRNA precursors. (a) Length distribution of known and novel miRNA precursors. (b) Distribution of known and novel miRNAs precursors based on genomic locations. (c) GC content (%) distribution of known and novel miRNA precursors. (d) Graph representing negative minimum free energy index (MFEI) in Kilo-calories (Kcal) of known and novel miRNAs precursors\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Fig. S3\u003c/strong\u003e Heatmap showing differentially expressed miRNAs and their inversely correlated targets that show differential expression. For each miRNA, only one corresponding target with the highest inverse correlation is shown. The color scale and values in the boxes represent log\u003csub\u003e2 \u003c/sub\u003etransformed RPM values for miRNAs and TPM values for their respective targets at each developmental stage\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Fig. S4\u003c/strong\u003e Heatmap showing the 83 miRNA-target gene modules involved in the regulation of seed size. Modules exhibiting inverse expression correlation between miRNA and their corresponding target are shown with an asterisk. Respective groups are also shown on the left. Expression values for both miRNAs (RPM) and targets (TPM) are transformed as log\u003csub\u003e2\u003c/sub\u003e (RPM +0.1) and log\u003csub\u003e2\u003c/sub\u003e (TPM +0.1), respectively\u003c/p\u003e","description":"","filename":"Jainetal2025SuppFigures260525.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7092705/v1/0d9f2e5bf092ff74d8ce731f.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Small RNA sequencing unveils predominant expression patterns and miRNA-target modules active during seed development in sorghum","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe grains of cultivated sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e) serve as a staple food for a large fraction of the global population. Many regions of Africa, Asia, and other continents utilize sorghum grains as a staple crop (Mundia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Besides, sorghum is also a prominent fodder and biofuel crop (Mathur et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khoddami et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mature sorghum grain comprises the seed coat, endosperm, and embryo. Like other cereals, the endosperm occupies the maximum portion of the mature grain. Nutritionally, sorghum seeds contain 55\u0026ndash;76% carbohydrates, 7\u0026ndash;15% proteins, ~\u0026thinsp;3% lipids, and small quantities of vitamins, minerals, and phenolic compounds (Khoddami et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Due to high dietary fiber, essential elements, and antioxidant content, sorghum seeds are also considered nutraceuticals (Tanwar et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, sorghum grains have a high economic value, with grain weight and size as major contributors to grain yield (Takanashi \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, starch content, starch quality, seed shattering, etc. are also agriculturally important seed traits in sorghum.\u003c/p\u003e\u003cp\u003eGrain size has been a target of artificial selection since domestication. However, further improvement in grain size through traditional breeding approaches is challenging (Liu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, it is imperative to dissect the molecular basis of grain development and to identify candidates for genetic improvement in sorghum. MicroRNAs have been experimentally demonstrated to act as key modulators of seed traits (Dhaka and Sharma \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A recent study reported the role of miR159 in positively regulating grain size and weight in maize (Wang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zma-miR159 regulates cell division and proliferation in endosperm cells by targeting MYB transcription factor genes, \u003cem\u003eZmMYB74\u003c/em\u003e and \u003cem\u003eZmMYB138\u003c/em\u003e. Overexpression of Zma-miR159 or loss of function of \u003cem\u003eZmMYB74\u003c/em\u003e lead to increased grain size. Another recent study in maize dissected the role of miR169-\u003cem\u003eZmNF-YA13 (NUCLEAR FACTOR-Y SUBUNIT A13)\u003c/em\u003e module in determining seed size and weight (Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Zma-miR169o regulates the number of endosperm cells and hence seed size by negatively regulating \u003cem\u003eZmNF-YA13\u003c/em\u003e expression. Overexpression of zma-miR169o in maize led to enhanced seed size and weight (Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies suggest that miRNAs play key role in regulating seed size and weight as top-tier regulators.\u003c/p\u003e\u003cp\u003eConsidering the significance of miRNAs in regulating seed size and agronomic attributes, small RNA transcriptome profiling has been extensively leveraged to identify miRNA target modules during seed development. miRNA expression profiling from developing seeds has been used to identify important candidates in various crops. For instance, miRNA profiling from 4 stages of seed development in maize highlighted important miRNA candidates associated with embryogenesis and nutrient storage (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly in the oilseed crop \u003cem\u003eB. juncea\u003c/em\u003e, miRNAs expressed during different seed stages have been used to shortlist candidates for seed size, seed coat color, and oil content (Jain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In chickpea, miRNA expression dynamics using small- and bold-seeded varieties revealed important candidates for seed weight (Khemka et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, miRNA profiling from embryos and endosperm collected at different maturation stages in rice provided miRNA-target modules associated with seed dormancy and their relationship with phytohormones including auxin, abscisic acid, and ethylene (Park et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn sorghum, the role of miRNAs in regulating grain development and associated agronomic traits remains unexplored. Here, we address this lacuna and report identification, \u003cem\u003ein silico\u003c/em\u003e characterization, and analysis of sorghum seed miRNAome. Further, we integrated the small RNA data with our recently published transcriptome data from developing sorghum grains to identify the most suitable miRNA-target modules for grain improvement (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mapping of orthologous gene functions and quantitative trait loci (QTLs) on these modules underlined high-priority candidates for engineering seed traits.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eGrowing of plants and sample harvesting\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSorghum bicolor\u003c/em\u003e variety M35-1 plants was grown under open field conditions at Jawaharlal Nehru University, New Delhi, India, from February to June 2021 in an open field. Mature pistils 1\u0026ndash;2 days before pollination (1\u0026ndash;2 DBP) and developing seeds at 0\u0026ndash;2, 10, 20, and 30 days after pollination (DAP) were collected, as described in our previous study (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The tissue samples were snap-frozen in liquid nitrogen and stored at -80\u0026deg;C until further use.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA extraction and small RNA profiling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was extracted from all the samples using TRIzol\u0026trade; (Ambion) reagent, as described previously (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Genomic DNA was removed using Turbo DNA-free\u0026trade; Kit (AM1907, Invitrogen) according to the manufacturer\u0026rsquo;s instructions. The concentration and quality of the purified RNA samples were determined using Nanodrop\u0026trade; Spectrophotometer (Nanodrop\u0026trade; 2000), agarose gel electrophoresis, Qubit fluorometer (Thermo Fisher Scientific, USA), and Agilent 2200 TapeStation. Two biological replicates of pistils collected at 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP, and three biological replicates of developing seeds collected at 10, 20, and 30 DAP stages, with RNA integrity number (RIN) values\u0026thinsp;\u0026gt;\u0026thinsp;7, were used for small RNA library preparation using QIAseq\u0026reg; miRNA Library Kit (Qiagen, Maryland, U.S.A.), as per the manufacturer\u0026rsquo;s guidelines. A total of 13 small RNA libraries were prepared. Thereafter, 50 bp single-end small RNA sequencing was performed using the Illumina NovaSeq 6000 sequencing platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSmall RNA sequencing data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe quality of the small RNA sequencing data was checked using FastQC (Andrews \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Next, Cutadapt v.4.1 (Martin \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to remove the adapter sequences and to select the reads ranging from 20 to 24 nucleotides (nt) in length. For subsequent analysis, low-quality reads were removed using the fastq_quality_filter tool from the FASTX-toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hannonlab.cshl.edu/fastx_toolkit/commandline.html\u003c/span\u003e\u003cspan address=\"http://hannonlab.cshl.edu/fastx_toolkit/commandline.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a threshold of less than 70% of bases having a Phred score\u0026thinsp;\u0026lt;\u0026thinsp;30. Subsequently, redundant reads were removed from each library by collapsing the reads using fastx_collapser from the FASTX-toolkit. Finally, the unique reads were utilized to identify miRNAs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of miRNAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe unique reads were first screened against the Rfam database v.14.9 using the miRDeep-P2 program (Kuang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reads mapping to housekeeping non-coding RNAs, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs) were removed. The remaining reads were mapped to the \u003cem\u003eSorghum bicolor\u003c/em\u003e reference genome v.5.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/info/Sbicolor_v5_1\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/info/Sbicolor_v5_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, miRNAs were predicted using miRDeep-P2 as described in (Jain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The miRNAs were aligned against the miRBase database v.22.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirbase.org/\u003c/span\u003e\u003cspan address=\"https://www.mirbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Bowtie v.1.3.1 (Langmead \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), allowing one mismatch. Those mapping to miRBase were considered as known miRNAs, while the remaining were predicted as novel miRNAs. To increase the stringency of prediction, only miRNAs present in at least two independent sRNA libraries were used for further analysis. For nomenclature, the known miRNAs were named with the prefix \u0026lsquo;sbi\u0026rsquo; (\u003cem\u003eSorghum bicolor\u003c/em\u003e), followed by the miRNA family name. The different mature miRNA sequences, within the same miRNA family, were differentiated by adding a numeric suffix after the family name. For instance, sbi-miR156.1, sbi-miR156.2, and so on. On the other hand, the novel miRNAs were named from sbi-novel-miR1 to sbi-novel-miR154.\u003c/p\u003e\u003cp\u003eThe minimum free energy (MFE) and minimum free energy index (MFEI) of miRNA precursors were calculated as detailed in our previous study (Jain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The genomic location of miRNA precursors was determined using bedtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bedtools.readthedocs.io/en/latest/\u003c/span\u003e\u003cspan address=\"https://bedtools.readthedocs.io/en/latest/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression analysis of miRNAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe raw counts of unique miRNAs were normalized as reads per million (RPM) using the formula:\u003c/p\u003e\u003cp\u003eNormalized expression = (Actual miRNA read count/Total count of clean reads)*10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePairwise differential expression was carried out using the R package DESeq2 v.1.34.0 (Love et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The miRNAs exhibiting log\u003csub\u003e2\u003c/sub\u003e (fold change) \u0026ge;1 or \u0026le;-1 and FDR (false discovery rate) \u0026le;0.05 were considered as differentially expressed. The miRNAs were classified into different groups based on stage-specific or peak expression as compared between different stages. The miRNAs expressed only in any particular stage, while exhibiting 0 RPM in the other stages were considered as stage-specific. The heatmaps for visualizing expression patterns were prepared using MultiExperiment Viewer (MeV) (Howe et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrediction of miRNA targets and construction of miRNA-mRNA networks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo predict putative targets of miRNAs, we used a set of 31,337 genes retrieved from our transcriptomic dataset generated from sorghum M35-1 seeds at the same stages of development (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Putative target genes were first predicted using the psRNATarget web server (Dai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), using default parameters of schema v2 (2017 release). The top targets were limited to 50, and \u003cem\u003eSorghum bicolor\u003c/em\u003e transcripts (annotated in v3.1.1 Phytozome) were selected in the psRNATarget for target prediction. The identified targets were further shortlisted by mapping to our in-house transcriptome data. We had earlier collated information on pathways, transcription factors (TFs), orthologs, QTLs, and candidate genes for grain size and other grain traits in sorghum (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This information was retrieved for the target genes for functional annotation analysis. Pathway and TF enrichment analysis was performed using a hypergeometric test, with a p-value cut-off of \u0026le;0.05 and a false discovery rate (FDR) of \u0026le;0.05. The transcriptome data of predicted targets was obtained from our previous study (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the correlation in expression of small RNAs and corresponding targets was assessed in Microsoft Excel. Inversely correlated targets were identified using the Pearson correlation coefficient cut-off \u0026le;-0.7. The miRNA-target networks of selected modules were visualized using Cytoscape (v.3.9.1) (Shannon et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cb\u003eIdentification and characterization of miRNAs from pistils and developing seeds of\u003c/b\u003e \u003cb\u003eSorghum bicolor\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe had previously investigated transcriptional dynamics of pistils collected 1\u0026ndash;2 days before pollination (DBP) and developing seeds at four different stages viz., 0\u0026ndash;2 days after pollination (DAP), 10 DAP, 20 DAP, and 30 DAP in \u003cem\u003eSorghum bicolor\u003c/em\u003e M35-1 (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These stages corresponded to pre-pollination pistils (1\u0026ndash;2 DBP), pollination, fertilization and early embryogenesis (0\u0026ndash;2 DAP), soft dough (10 DAP), hard dough (20 DAP), and physiological maturity (30 DAP) stages of seed development (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, we performed small RNA sequencing from these five stages and identified the miRNAs and miRNA-target modules associated with seed development.\u003c/p\u003e\u003cp\u003eA total of 13 libraries were generated. The Pearson\u0026rsquo;s correlation coefficient between biological replicates for all libraries ranged from 0.7 to 0.9, except for one library (30 DAP_3), which exhibited low correlation and hence, was removed from further analysis (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). The remaining 12 libraries yielded\u0026thinsp;\u0026gt;\u0026thinsp;175\u0026nbsp;million raw reads (Supplementary Table\u0026nbsp;1). After adaptor trimming, length filtering (20\u0026ndash;24 nt), and removal of low-quality reads, ~\u0026thinsp;86\u0026nbsp;million high-quality reads were retained. These were collapsed to 19.58\u0026nbsp;million unique reads (Supplementary Table\u0026nbsp;1). The length distribution of unique sRNA reads across all stages peaked at 24-nt (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb), conforming to that reported in other plants (Meijer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor miRNA identification, the unique reads mapping to noncoding RNAs such as rRNAs, tRNAs, snRNAs, and snoRNAs were removed. The remaining reads were mapped to the sorghum genome, and the predicted miRNAs were further mapped to Viridiplantae mature miRNA sequences from the miRBase v.22.1. Additionally, only miRNAs present in at least two independent sRNA libraries were considered for further analysis. This analysis identified a total of 226 miRNAs, of which 72 were mapped to the known miRNAs, while the remaining 154 were designated as novel miRNAs (Supplementary Table\u0026nbsp;2). These miRNAs originated from 467 putative precursors, with 147 and 320 precursors for known and novel miRNAs, respectively (Supplementary Table\u0026nbsp;2). The precursor lengths for the known miRNAs ranged from 57 to 220 nt, with an average precursor length of 110 nt, while the precursor lengths for the novel miRNAs varied from 43 to 227 nt, with an average length of 114 nt (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). The miRNA precursors mapped to five genomic locations, including intergenic (59%), intronic (28%), CDS (6%), 5' UTR (3.6%), and 3' UTR (3%) regions with the highest number of miRNA precursors originating from intergenic regions (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). The GC content in known and novel miRNA precursors varied from 24.6 to 71.6% and 18.3 to 84.1%, respectively (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). The negative Minimum free energy Index (MFEI) for the known miRNA precursors ranged from 0.7 to 3.3 Kcal with an average MFEI value of -2 Kcal, while the negative MFEI value for the novel miRNA precursors varied from 0.5 to 6.8 Kcal, with an average of -2 Kcal (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eThe length distribution of miRNAs showed that both known and novel miRNAs ranged from 20 to 24 nt in length, with the highest frequency of 21 nt miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The base distribution of the mature miRNA sequence revealed \u0026lsquo;U\u0026rsquo; as the most frequently occurring first base for both known and novel miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). These findings are consistent with those reported in other plant species (Xue et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Curaba et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A total of 33 known miRNA families were detected in our data. Among these, miR156, miR169, miR171, and miR172 were the largest with 5 members each, followed by miR164, miR166, miR167, and miR396 families with 4 members each. The miRNA families miR399, and miR408 had 3 members each while miR159, miR160, miR393, miR395, miR398, miR2118, and miR5564 had 2 members each. The remaining miRNA families including miR168, miR319, miR384, miR390, miR394, miR397, miR479, miR528, miR529, miR530, miR827, miR1432, miR5169, miR5385, miR6223, and miR6234 had single member each (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Notably, the high number of isoforms of miR156, miR169, miR171, and miR172 families has been reported in developing seeds of other crops as well (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe chromosomal distribution of miRNAs was uneven, with chromosome 02 harboring the maximum number of total (44) and known miRNAs (15). On the other hand, the maximum number of novel miRNAs was observed on chromosome number 1 (32) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Overall, the sequence features and distribution pattern of sorghum miRNAs were consistent with those reported in other crop species.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegrated analysis with transcriptome for target identification and functional annotation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used the transcriptome data generated from the same stages of seed development in our earlier study (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to predict the miRNA targets using the psRNAtarget server. A total of 4504 target genes and 6640 miRNA-target modules were identified. Targets were identified for all 226 miRNAs, with the number of targets for each miRNA ranging from 17 to 40 (Supplementary Table\u0026nbsp;3). Based on the ortholog information, literature analysis, pathway mapping, and TF information of sorghum genes previously compiled for our recent transcriptome study (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we assigned functional categories to all the target genes. Among the 4504 targets, rice and \u003cem\u003eArabidopsis\u003c/em\u003e orthologs were identified for 3206 and 2414 genes, respectively (Supplementary Table\u0026nbsp;3). Further, 2511 genes could be assigned MapMan pathways, and 307 encoded transcription factors (Supplementary Table\u0026nbsp;3). The functional annotation analysis based on cross-species comparison implicated 32 target genes in regulating grain size and another 10, in modulating starch content, 3 in seed vigor, and 1 in both seed dormancy and seed shattering (Supplementary Tables\u0026nbsp;6 \u0026amp; 7).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMagnitude and distribution of miRNA expression during seed development in sorghum\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis of expression patterns of the miRNAs in all five temporal stages revealed 42 (19%) of the 226 miRNAs as common to all five stages, whereas 15 (6.6%), 41 (18%), and 80 (35.3%) miRNAs were only detected in any four, three, and two stages, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Table\u0026nbsp;2). Further, 48 (21.2%) miRNAs were stage-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Table\u0026nbsp;2). The stage-wise expression dynamics showed that a larger proportion of miRNAs exhibited high expression values\u0026thinsp;\u0026gt;\u0026thinsp;1000 RPM in the 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP stages, while the 10, 20, and 30 DAP stages instead had a higher proportion of low expression miRNAs with expression values ranging from 10\u0026ndash;100 RPM (Supplementary Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). A total of 15 miRNAs with an average RPM\u0026thinsp;\u0026gt;\u0026thinsp;10,000 were identified as top expressed miRNAs in our data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Of these, 12 were known, while 3 were novel. Notably, among the known miRNAs, there were 3 family members from miR166, 2 family members each from miR5564, miR167, and miR171, and one member each from miR168, miR156, and miR319. The novel miRNAs were sbi-novel-miR98, sbi-novel-miR144, and sbi-novel-miR17 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Supplementary Table\u0026nbsp;2). Several of these high-expressing miRNAs in developing sorghum seeds have established roles in seed development. For example, miR156, miR166, miR167, and miR168 regulate embryo development (Rodrigues and Miguel \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They are also reported to express highly during seed development in other crop plants (Bai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, miR5564, the miRNA with the highest expression in our data has not been reported to express in developing seeds of any other plant species. It has earlier been reported to exhibit high expression in developing sorghum anthers (Dhaka et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and leaves (Puli et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It showed very high expression in our data, with maximum accumulation at seed maturity. Moreover, most of the targets of miR5564 code for PPR (Pentatricopeptide repeat) proteins. In \u003cem\u003eArabidopsis\u003c/em\u003e, several PPR proteins have been reported to regulate embryogenesis (Cushing et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thus, considering evidence of PPR-mediated regulation of embryogenesis, and detection of miR5564 in sorghum seeds makes miR5564 an interesting candidate for further exploration. Apart from miR5564, the highly abundant novel miRNAs sbi-novel-miR98, sbi-novel-miR144, and sbi-novel-miR17 are also important candidates for further investigation, as their high expression is suggestive of their importance for seed development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential expression of miRNAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDifferential expression analysis at each stage with respect to the preceding stage obtained a total of 102 differentially expressed miRNAs exhibiting log\u003csub\u003e2\u003c/sub\u003e fold change \u0026le;-1 or \u0026ge;\u0026thinsp;1 (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;4). Among these, 15, 64, 15, and 48 miRNAs were differentially expressed in the comparison sets of 0\u0026ndash;2 DAP \u003cem\u003evs\u003c/em\u003e 1\u0026ndash;2 DBP, 10 DAP \u003cem\u003evs\u003c/em\u003e 0\u0026ndash;2 DAP, 20 DAP \u003cem\u003evs\u003c/em\u003e 10 DAP, and 30 DAP \u003cem\u003evs\u003c/em\u003e 20 DAP, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). None of the miRNAs were common to all the comparison sets, while 36 miRNAs were detected in more than one comparison set. In the 0\u0026ndash;2 DAP \u003cem\u003evs\u003c/em\u003e 1\u0026ndash;2 DBP comparison, 11 miRNAs were upregulated, while 4 miRNAs were downregulated. In the 10 DAP \u003cem\u003evs\u003c/em\u003e 0\u0026ndash;2 DAP comparison, 40 miRNAs were upregulated, while 24 were downregulated. In the 20 DAP \u003cem\u003evs\u003c/em\u003e 10 DAP stages, 8 and 7 miRNAs were upregulated and downregulated, respectively, while in the 30 DAP \u003cem\u003evs\u003c/em\u003e 20 DAP comparison, 19 and 29 miRNAs were upregulated and downregulated, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These results highlight the maximum difference in miRNA profiles during 0\u0026ndash;2 DAP to 10 DAP transition and then 20 DAP to 30 DAP transition. This is consistent with what we observed in our transcriptomic study from these stages (Jain et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther, an integrated expression analysis of miRNAs and their targets based on the expression profiles of the miRNAs and respective targets showed 678 miRNA-target modules (127 miRNAs and 596 targets) exhibiting strong inverse correlation (Pearson correlation coefficient \u0026le; -0.7) (Supplementary Table\u0026nbsp;3). Among these, 52 miRNAs and 187 targets, constituting 195 modules, were differentially expressed (Supplementary Fig. S3). This finding was not surprising, as multiple regulatory pathways can impact mRNA levels at a given time. Therefore, one may not observe an inverse correlation in miRNA-mRNA levels during developmental stages (Diener et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGrouping of miRNAs based on expression patterns reveals candidate miRNAs and miRNA-target modules regulating grain traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we further investigated the expression profiles to correlate the miRNA expression dynamics with functional annotations. First, we classified all the miRNAs into five groups based on the respective stages in which they exhibited specific or peak expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Next, we integrated information obtained from literature and public databases for cross-species orthologs analysis and QTL mapping to obtain functional insights. The biological relevance of these distinct patterns was inferred based on the experimentally verified functions assigned to the homologs of these miRNAs and their targets in other plant species. Interestingly, the majority of the known miRNAs also exhibited differential expression, indicating a significant shift in their expression in subsequent stages of seed development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on literature survey, a total of 19 known miRNA families have been implicated in the regulation of grain size so far. Our data contained a total of 52 miRNAs from these families. These miRNAs targeted 1011 genes, constituting a total of 1493 miRNA-target modules. Furthermore, we identified 14 novel miRNAs that target genes involved in the regulation of seed size, comprising 14 novel miRNA-target modules for seed size. Thus, 1507 miRNA-target modules were identified as candidates for seed size regulation (Supplementary Table\u0026nbsp;5). Of these 1507 modules, 83 candidate miRNA-target modules are those for which the entire module has been previously characterized for grain size regulation and hence were highlighted as high-priority candidates in each group (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Fig. S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBesides grain size, 12 miRNAs and 10 target genes constituting 12 modules were identified as regulators of starch content, while 5 miRNAs, and 1 target gene constituting 5 modules regulating seed dormancy, 2 miRNAs, and 3 target genes constituting 3 modules regulating seed vigor, and 3 miRNAs, and 1 target gene constituting 3 modules regulating seed shattering were identified (Supplementary Table\u0026nbsp;7). These modules were also prioritized in each group for the regulation of seed traits.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup 1 miRNAs exhibit predominant expression in pre-pollination pistils\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGroup 1 consisted of 36 miRNAs exhibiting predominant expression in pre-pollination pistils, as they were either 1\u0026ndash;2 DBP-specific or exhibited peak expression at 1\u0026ndash;2 DBP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the known miRNA families, miR319, miR384, miR394, and miR6223 were exclusive to this group. Some of these miRNA families have been previously detected from reproductive tissues in other crop species as well. For example, miR394 exhibits high expression in floral buds with low expression in later seed stages in \u003cem\u003eB. napus\u003c/em\u003e and has been associated with floral and seed development (Song et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, miR171 has also been shown to accumulate in the reproductive organs, with high expression in young spikes and ovaries in barley (Curaba et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). One of the group 1-specific novel miRNAs, sbi-novel-miR49, exhibited very high expression (\u0026gt;\u0026thinsp;2210 RPM), and therefore, is an interesting candidate for further exploration.\u003c/p\u003e\u003cp\u003eFunctional annotation analysis of target genes of group 1 miRNAs revealed phytohormone action (auxin), carbohydrate metabolism, plant organogenesis, cell division, and RNA biosynthesis as enriched pathway categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Supplementary Table\u0026nbsp;8). miRNA-mediated regulation of auxin signaling during embryo development has previously been established in barley (Bai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Several TF families including MADS, HD-ZIP, GRF, GRAS, and NF-YA were enriched in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Table\u0026nbsp;9). In rice, MADS-box genes express abundantly in ovaries and early hours after fertilization, but their expression declines 48 hours after fertilization conforming with the pattern observed in this study (Paul et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This indicates that miRNAs may be crucial factors in the downregulation of MADS-box genes during the transition from pre- to post-fertilization transition in cereals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the candidate miRNA/target gene information, we identified 16 known and 1 novel miRNA and 366 corresponding targets, comprising 473 miRNA-target modules as candidates for grain size in this group (Supplementary Table\u0026nbsp;5). Among these, 24 were high-priority modules as both the miRNAs and the corresponding target genes have been characterized for regulation of grain size in other crops (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These modules consisted of known miRNAs of miR156, miR160, miR164, miR167, miR169, miR171, and miR396 families (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). MiR156-\u003cem\u003eSPL12 (SQUAMOSA PROMOTER-BINDING-PROTEIN LIKE 12)\u003c/em\u003e module has been earlier reported to regulate seed size in rice (Zhang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The perturbation of the miR160-\u003cem\u003eARF18 (AUXIN RESPONSE FACTOR 18)\u003c/em\u003e module also leads to modulation of grain size and starch content (Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). MiR164-\u003cem\u003eNAC\u003c/em\u003e module regulates expansin genes which control seed expansion in maize (Zheng et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another crucial module is miR167-\u003cem\u003eARF12 (AUXIN RESPONSE FACTOR 12)\u003c/em\u003e. In rice, this module has been shown to mediate auxin and brassinosteroid signaling for the regulation of cell division in the developing grains. miR167 acts downstream of miR159, and targets \u003cem\u003eOsARF12\u003c/em\u003e, which in turn activates \u003cem\u003eOsCDKF (CYCLIN-DEPENDENT KINASE F)\u003c/em\u003e, a positive regulator of cell proliferation during grain filling. Thus, suppression of miR167, or overexpression of \u003cem\u003eARF12\u003c/em\u003e leads to an increase in seed size in rice (Zhao et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, miR169-\u003cem\u003eNF-YA13\u003c/em\u003e has been shown to regulate grain size in maize. miR169 targets \u003cem\u003eNF-YA13\u003c/em\u003e, which further positively regulates \u003cem\u003eYUC1 (YUCCA 1)\u003c/em\u003e positively. \u003cem\u003eYUC1\u003c/em\u003e modulates auxin signaling and enhances cell division and expansion in developing grains (Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, miR171-\u003cem\u003eHAM (HAIRY MERISTEM)\u003c/em\u003e regulates grain weight and other traits in rice (Um et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOf all these modules, miR396-\u003cem\u003eGRF (GROWTH REGULATING FACTOR)\u003c/em\u003e is one of the most well-characterized modules for miRNA-mediated regulation of seed size in both monocots and dicots (Liebsch and Palatnik \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In rice, \u003cem\u003eGRF4\u003c/em\u003e positively regulates cell division positively during grain development. \u003cem\u003eGRF4\u003c/em\u003e is the causal gene for rice grain size QTL \u003cem\u003eGS2 (GRAIN SIZE ON CHROMOSOME 2)\u003c/em\u003e, and a 2 bp mutation in its miR396-target region leads to heavier grains (Duan et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, the sbi-miR396.2 target in our data, \u003cem\u003eGRF4/GS2 (GRAIN SIZE ON CHROMOSOME 2)/GL2 (GRAIN LENGTH 2)/PT2 (PANICLE TRAIT 2)/LGS1 (LARGE GRAIN SIZE 1)\u003c/em\u003e (Sobic.004G269900), colocalized with the grain size QTL \u003cem\u003eQGWGT4.17\u003c/em\u003e (Supplementary Table\u0026nbsp;10).\u003c/p\u003e\u003cp\u003eTherefore, these modules can be effectively utilized for engineering grain size in sorghum. Besides, the module sbi-novel-miR121-\u003cem\u003eISA2 (ISOAMYLASE 2)\u003c/em\u003e (Sobic.009G127500) emerged as a candidate for grain starch content (Supplementary Table\u0026nbsp;7), as \u003cem\u003eISA2\u003c/em\u003e is involved in starch synthesis in sorghum (Hashimoto et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup 2 miRNAs exhibit predominant expression in post-pollination ovaries\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGroup 2 comprised 38 miRNAs with 24 known and 14 novel miRNAs that were either specifically expressed in both 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP stages, or 0\u0026ndash;2 DAP-specific, or showed 0\u0026ndash;2 DAP-peak (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Several miRNA families including miR172, miR395, miR397, miR399, miR529, miR1432, miR2118, miR5169, and miR5385 were exclusive to this group. Interestingly, all five members of the miR172 family were present in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Their expression profile indicates that miR172 is important for floral and early seed development, but its expression declines in the subsequent seed stages. In barley, miR172 targets \u003cem\u003ecly1 (CLEISTOGAMY 1)\u003c/em\u003e gene and regulates cleistogamy (Anwar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We also noted a very interesting parallel in the developing grains of maize (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A similar expression pattern was observed for three group 2 miRNAs viz., miR172, miR395, and miR529, with high expression in the early stage of maize seed development (6 DAP), and a decrease in expression after that. Therefore, these miRNAs may have conserved roles in regulating early embryogenesis during seed development. Further, miR2118 has earlier been reported to exhibit high expression in developing anthers and regulate male fertility in cereal crops (Song et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhai et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We had also observed high expression of this miRNA in developing sorghum anthers (Dhaka et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, nothing is known about its possible involvement in regulating female gametophyte development. Our data showed that both members of miR2118 exhibit significant expression in 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP stages. It would be interesting to explore their role in female gametophyte and early seed development. Another exclusively detected family of this group was miR5385 with sbi-miR5385.1 detected in 0\u0026ndash;2 DAP stage only. Previously, this miRNA has been associated with drought stress at the seedling stage in sorghum (Katiyar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, its role in the regulation of seed development is unknown. MiR5169 from this group has been earlier associated with floral and gametophyte development in \u003cem\u003eXanthoceras sorbifolium\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has also been shown to have possible involvement in the regulation of male fertility in wheat (Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Since this miRNA has not been previously characterized, and its targets are unknown, it is a good candidate for further characterization.\u003c/p\u003e\u003cp\u003eThe miRNAs preferentially expressed in this group were enriched for targets associated with RNA biosynthesis, plant reproduction, nutrient uptake, protein biosynthesis, RNA processing, etc., (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Supplementary Table\u0026nbsp;8). The TF families enriched in this group include ARR, C2H2, and AP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Table\u0026nbsp;9). Notably, AP2 TFs are major regulators of embryo and endosperm development (Ohto et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOrthology and mapping analysis yielded 14 known and 2 novel miRNAs and 357 target genes, comprising 392 miRNA-target modules (Supplementary Table\u0026nbsp;5). Among these, 17 complete modules have characterized roles in regulating grain size (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These modules comprised several known miRNAs including miR159, miR167, miR171, miR172, miR396, miR408, miR529, and miR1432. MiR159, miR167, miR171, and miR396 are crucial seed size regulators, as described above. MiR172 targets \u003cem\u003eSNB\u003c/em\u003e and \u003cem\u003eOs06g43220 (IDS1; INDETERMINATE SPIKELET 1)\u003c/em\u003e in rice, and its overexpression leads to reduced seed weight apart from other floral and seed abnormalities (Zhu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). miR172-\u003cem\u003eSNB/SSH1 (SUPERNUMERARY BRACT/SUPPRESSION OF SHATTERING 1)\u003c/em\u003e module is also a proven regulator of seed shattering in rice (Jiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and wheat (Debernardi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, sbi-miR172.1 target in sorghum, \u003cem\u003eSSH1\u003c/em\u003e (Sobic.002G083600), colocalized with the grain size QTL \u003cem\u003eQGWGT2.2\u003c/em\u003e (Supplementary Table\u0026nbsp;10). The expression pattern of miR172 also corroborates with that reported in rice, as it was highly expressed in 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP stages in our data, but did not express in the seeds at 20 and 30 DAP stages. In rice also, it is highly expressed in developing panicles but showed low expression in 10 DAF grains (Zhu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The similar expression profile suggests a conserved function in sorghum as well.\u003c/p\u003e\u003cp\u003emiR408 targets \u003cem\u003ePlantacyanin\u003c/em\u003e and \u003cem\u003eLaccase 13\u003c/em\u003e in \u003cem\u003eArabidopsis\u003c/em\u003e and is a negative regulator of seed size (Song et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). miR529 regulates grain size, seed dormancy, and other agronomic traits in rice (Yan et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the miR1432-\u003cem\u003eACOT\u003c/em\u003e (\u003cem\u003eAcyl-CoA thioesterase)\u003c/em\u003e module has been very well established for its role in seed development in rice (Zhao et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, it was interesting to note that despite the high expression of miR1432 in our data we did not identify \u003cem\u003eACOT\u003c/em\u003e or any other related thioesterase protein as its putative target. However, we identified another module in our data, miR1432-\u003cem\u003eEFH1 (EF-HAND FAMILY PROTEIN 1)\u003c/em\u003e, which is associated with maintaining the balance of grain yield and immunity (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Further, sbi-miR2118.2 was predicted to target \u003cem\u003eSh1 (SHRUNKEN 1)\u003c/em\u003e (Sobic.010G072300), suggesting its role in regulating starch content (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup 3 miRNAs exhibit predominant expression during early embryo and endosperm development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis group comprised 36 miRNAs with predominant expression in the 10 DAP stage. These were 0\u0026ndash;2 DAP and 10 DAP-specific or 10 DAP-specific, or exhibited 10 DAP-peak. The highest-expressing miRNAs in this group were sbi-miR171.3 and sbi-novel-miR98. They exhibited little or no expression in the 1\u0026ndash;2 DBP and 0\u0026ndash;2 DAP stages, and the highest expression in 10 DAP, which declined in subsequent stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This pattern suggests their role in the regulation of early seed development.\u003c/p\u003e\u003cp\u003eIn group 3, none of the known miRNA families were detected exclusively, but the miRNAs miR159, miR169, and miR171 families, represented here, have well-known roles in embryo and endosperm development (Zhao et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Takanashi et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). miR159.2 is a good candidate for the regulation of grain filling in this group with predominant expression in the 10 DAP stage. It has earlier been reported to express highly in rice and tartary buckwheat seeds as well (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Its role in endosperm proliferation has been experimentally shown in \u003cem\u003eArabidopsis\u003c/em\u003e (Zhao et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe mRNA targets of miRNAs of this group have been associated with DNA damage response, protein translocation, cell wall organization, etc., (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Supplementary Table\u0026nbsp;8) and with significant representation of the CPP TF family (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Table\u0026nbsp;9). During endosperm development, protein translocation plays an important role in determining grain quality (Roustan et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, these targets may be involved in the regulation of endosperm reorganization during seed development in sorghum.\u003c/p\u003e\u003cp\u003eWe also identified 3 known and 3 novel miRNAs and 94 corresponding target genes, comprising 94 miRNA-target modules as candidates for grain size (Supplementary Table\u0026nbsp;5). Of these, 6 modules are the high-priority modules for the regulation of grain size (Supplementary Table\u0026nbsp;6, Supplementary Fig.\u0026nbsp;4). These comprised miR159, miR169, and miR171. Notably, two targets for these miRNAs, sbi-miR169.1-\u003cem\u003eDA1\u003c/em\u003e (Sobic.010G064600) and sbi-miR171.3-\u003cem\u003eNF-YB1 (NUCLEAR FACTOR-Y SUBUNIT B1)\u003c/em\u003e (Sobic.008G064100), colocalize with grain size QTL (Supplementary Table\u0026nbsp;10). Additionally, sbi-novel-miR129-\u003cem\u003eSBEIIa (STARCH BRANCHING ENZYME IIa)\u003c/em\u003e (Sobic.006G066800) was identified as a potential regulator of grain starch content. (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup 4 miRNAs exhibit predominant expression during grain-filling stages\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis was the largest group with 66 miRNAs. These miRNAs were 10 DAP and 20 DAP-specific, or 20 DAP-specific, or showed peak expression at 20 DAP. This group harbored five critical miRNA families, miR168, miR398, miR528, miR530, and miR6234, as specific to this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Some of these candidates are the signatures of grain filling. For example, miR398 and miR528 have been implicated in regulating seed filling in rice as well (Peng et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). miR530 is highly expressed in rice embryos and positively regulates cell division and expansion in the developing seeds (Sun et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe targets for this group were overrepresented from key pathway categories like protein homeostasis, solute transport, cell wall, secondary metabolism, etc., (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Supplementary Table\u0026nbsp;8), suggesting that miRNAs of this group may be crucial for regulating metabolic and transport pathways during grain filling. The HSF, bZIP, and NAC TF families also were enriched in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Table\u0026nbsp;9). miR164- and miR167-mediated targeting of NAC TFs has also been demonstrated during grain filling in rice (Peng et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe grain size candidate identification highlighted 10 known and 5 novel miRNAs and 295 targets, constituting 296 miRNA-target modules (Supplementary Table\u0026nbsp;5). Of these, 16 are the high-priority modules (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with known miRNAs from miR156, miR164, miR167, miR168, miR398, and miR530 families. MiR168 regulates \u003cem\u003eAGO18 (ARGONAUTE 18)\u003c/em\u003e and \u003cem\u003eAGO1\u003c/em\u003e, which further regulate grain size regulatory miRNAs, miR396 and miR529. Further, miR168-\u003cem\u003eAGO\u003c/em\u003e module regulates many other agronomic traits directly and indirectly (Zhou et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). MiR168 exhibited high expression in both 10 DAP and 20 DAP stages, with a peak at 20 DAP, and sbi-miR168.1 targets Sobic.010G276600, an ortholog of rice \u003cem\u003eOsAGO1d\u003c/em\u003e indicating a conserved pathway in sorghum as well. Further, miR398 targets \u003cem\u003eCSD1-2 (Cu/Zn Superoxide Dismutases1-2)\u003c/em\u003e and \u003cem\u003eCCS\u003c/em\u003e (copper chaperone of CSD) in rice, and overexpression of miR398 causes an increase in grain size (Lu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, miR530-\u003cem\u003ePL3 (PLUS3 DOMAIN-CONTAINING PROTEIN)\u003c/em\u003e module acts downstream of \u003cem\u003ePIL15 (PHYTOCHROME-INTERACTING FACTOR-LIKE 15)\u003c/em\u003e to regulate grain size. Overexpression of miR530 and knockdown of \u003cem\u003ePL3\u003c/em\u003e both lead to a decrease in grain size (Sun et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, the modules sbi-miR530.1-\u003cem\u003eGS9 (GRAIN SIZE ON CHROMOSOME 9)\u003c/em\u003e (Sobic.002G220700), sbi-novel-miR20-\u003cem\u003eglHAT1 (GNAT-LIKE HISTONE ACETYLTRANSFERASE)/GW6a (GRAIN WEIGHT ON CHROMOSOME 6a)\u003c/em\u003e (Sobic.010G210100), and sbi-novel-miR99-\u003cem\u003eWRKY53\u003c/em\u003e (Sobic.009G100500) consisted of genes colocalizing with grain size QTLs (Supplementary Table\u0026nbsp;10). Further, we determined 12 miRNA-target modules as candidates for regulating other grain traits in sorghum, with 7 and 3 modules involved in the regulation of grain starch content and seed vigor, respectively. (Supplementary Table\u0026nbsp;7). Interestingly, one of these, miR160, which putatively targets starch metabolism gene \u003cem\u003eSSIIIb (SOLUBLE STARCH SYNTHASE IIIb)\u003c/em\u003e in our data, has also been identified as a candidate miRNA regulating starch synthesis in foxtail millet in a recent study (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). miR168-\u003cem\u003eAGO1\u003c/em\u003e module is also experimentally determined to be a critical regulator of seed vigor in rice (Zhou et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MiR398-\u003cem\u003eCSD1\u003c/em\u003e is also involved in the regulation of seed vigor in response to copper sulphate stress (Lu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, miRNAs of this group are likely very important for regulating seed size, starch content, and vigor.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup 5 miRNAs exhibit predominant expression during seed maturation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis group contained 50 miRNAs with predominant expression in the later stages of seed development as they were 20 DAP and 30 DAP-specific, or 30 DAP-specific, or exhibited 30 DAP-peak (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The miRNA families exclusive to this group were miR390, miR479, miR827, and miR5564. In \u003cem\u003eB. napus\u003c/em\u003e also, miR390 was more prevalent in the mature seeds as compared to the developing seeds (Koerbes et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recently, miR390 has been shown to be a possible regulator of seed dormancy in rice (Park et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The expression of miR390 in rice also increases with seed maturity. It has also been identified as a candidate for regulating seed germination and seed storability in maize (Song et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that these known miRNA families may be important for the regulation of seed maturation and desiccation-related processes.\u003c/p\u003e\u003cp\u003eFor the miRNAs peaking at the 30 DAP stage, the targets showed enrichment of enzyme classification, lipid metabolism, protein modification, etc., (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Supplementary Table\u0026nbsp;8) and TFs of MYBs and SBP families (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Table\u0026nbsp;9). Several \u003cem\u003eSPL\u003c/em\u003e members are known to regulate seed dormancy (Miao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), suggesting that the targets of the miRNAs of group 5 may be associated with late seed development.\u003c/p\u003e\u003cp\u003eThe candidate identification highlighted 9 known and 3 novel miRNAs with 236 targets, comprising 252 miRNA-target modules associated with grain size (Supplementary Table\u0026nbsp;5). Among these, 20 modules are high-priority modules. These comprised miRNAs from miR156, miR164, miR167, miR169, miR171, miR408, and miR827 families (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). miR827-\u003cem\u003eSPX-MFS1 (SYG1/PHO81/XPR1- MAJOR FACILITY SUPERFAMILY 1)\u003c/em\u003e module has recently been associated with grain weight and other traits in rice (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Further, 5 of the targets (\u003cem\u003eSPL18, SPL16 /qGW8; GRAIN WIDTH 8, SPL14, ARF12, RGA1; RICE G PROTEIN ALPHA SUBUNIT 1/D1; DWARF 1\u003c/em\u003e, and \u003cem\u003eRGB1; RICE G PROTEIN BETA SUBUNIT 1\u003c/em\u003e) overlapped with grain size QTL regions (Supplementary Table\u0026nbsp;10).\u003c/p\u003e\u003cp\u003eFurther, two modules, sbi-miR827.1-\u003cem\u003eSh1\u003c/em\u003e (Sobic.010G072300) and sbi-novel-miR45-\u003cem\u003ePHO1 (STARCH PHOSPHORYLASE 1)\u003c/em\u003e (Sobic.001G083900) are candidates for grain starch content regulation as these target genes are known to be involved in starch biosynthesis in cereal grains (Chourey and Nelson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Satoh et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Interestingly, miR827 has recently been experimentally validated for the regulation of starch content in rice. STTM827 and Crispr-827 lines exhibit lower starch content and altered sugar quality in rice seeds (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our data, \u003cem\u003eSh1\u003c/em\u003e (Sobic.010G072300), which is a major regulator of sucrose biosynthesis and metabolism (Chourey and Nelson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), is a putative target of miR827.\u003c/p\u003e\u003cp\u003eFurthermore, the module sbi-miR156.1-\u003cem\u003eSPL14/IPA1 (IDEAL PLANT ARCHITECTURE 1)\u003c/em\u003e (Sobic.007G210200) is a candidate for the regulation of seed dormancy (Supplementary Table\u0026nbsp;7). In rice, the miR156-\u003cem\u003eIPA1\u003c/em\u003e module regulates seed dormancy by controlling GA signaling (Miao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, it is an important module for engineering pre-harvest sprouting in sorghum.\u003c/p\u003e\u003cp\u003eIt was also interesting to note that several members of the same miRNA family were assigned to different groups due to varied expression patterns. For example, miR156 and miR164 family members belonged to groups 1, 4, and 5. A similar finding has been previously reported in maize seeds, where Jin et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that different members of the same miRNA family may exhibit stage- or spatially-specific expression within the seeds.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of miRNA-target networks with shortlisted modules reveals common genes targeted by multiple miRNAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further elaborate the interrelationships between miRNA and targets regulating grain size, we constructed a miRNA-gene network regulating grain size in sorghum. We developed a miRNA-gene network by using all the 1507 modules identified in this study (Supplementary Table\u0026nbsp;5). Using the information of interconnected nodes through shared miRNAs and targets, we identified 1479 nodes and 2958 edges. Interestingly, we obtained a large network that showed interconnections between 19 miRNA families and several novel miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Of these, miR394 showed the higher number of connections through shared targets. It had targets shared with miR167, miR172, miR164, miR396, and miR1432. Other families including miR172, miR396, and miR408 were also connected with multiple miRNA families (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Table\u0026nbsp;11). Novel interplays were noticed using miRNA-gene network analysis. For example, sbi-novel-miR50 targeted the miR396-target \u003cem\u003eGRF4\u003c/em\u003e, and sbi-novel-miR97 targeted the miR172 target \u003cem\u003eSNB\u003c/em\u003e. We also found a total of 70 targets which were shared by more than one miRNA. Interestingly, miR156 and miR529 have nine shared targets. All of these were SPL genes, including the seed size regulators \u003cem\u003eSPL14, SPL16, SPL12, SPL18\u003c/em\u003e, and other \u003cem\u003eSPLs\u003c/em\u003e (Sobic.006G171000, Sobic.003G406600, Sobic.004G058900, Sobic.002G247800, and Sobic.005G120600) (Supplementary Table\u0026nbsp;11). miR156 and miR529 are well-known combinatorial regulators that target the same genes. They share sequence similarity and hence target the same genes to regulate seed development (Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding suggests that the identified miRNAs that possess shared targets likely function together to regulate sorghum seed development, allowing for more robust and complex regulation of gene expression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified and characterized sorghum seed miRNAome using five different developmental stages. The expression profiling and stage-wise grouping highlighted the miRNAs crucial for floral and female gametophyte development, early embryogenesis, embryo and endosperm development, grain filling, and seed maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The expression profiles showed that 22 miRNA families exhibited distinct group-wise profiles suggesting specific functions in each stage. The known functions of these exclusive families enabled the delineation of candidates for seed development. For instance, miR394 is associated with pistil development, miR172, miR295, and miR529 regulate early seed development, miR168, miR398, miR528, and miR530 are candidates for grain filling, while miR390, miR827, and miR5564 likely regulate seed maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Target identification, functional annotation analysis, and integrated analysis with transcriptome data further highlighted the pathways, TFs, and interplay of miRNA and target expressions during sorghum seed development. Moreover, 19 known miRNA families, 66 miRNAs, and 1019 targets comprising 1507 miRNA-target modules emerged as candidates based on the orthologs. Of these, 83 modules were selected as high-priority candidates as they have been experimentally validated for their role in seed development in other crops (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among these, miR156-\u003cem\u003eSPL14/16/18\u003c/em\u003e, miR167-\u003cem\u003eARF12\u003c/em\u003e, miR396-\u003cem\u003eGRF4/GS2/GL2\u003c/em\u003e, and miR172-\u003cem\u003eSSH1\u003c/em\u003e emerged as the most important as they also overlapped with sorghum grain size QTLs. Among the novel miRNAs, sbi-novel-miR20, 45, 49, 98, 99, 121, and 129 were identified as important candidates based on their expression patterns and target analyses. Apart from an expansive investigation of grain size, we also obtained 23 miRNA-target candidates for grain starch content and other agronomic traits.\u003c/p\u003e\u003cp\u003eThese modules are suitable for functional validations using knockout and overexpression of miRNAs, and target mimic analysis. The allelic variation in targets may also be investigated in the sorghum germplasm to correlate with trait variation. Some of these modules are also involved in regulating other aspects of plant growth and development, and therefore, specific strategies may be employed to manipulate seed size specifically when targeting these modules. Alternatively, instead of the manipulation of miRNA, target genes may be edited to yield desired goals. The in-depth characterization of shortlisted miRNA-mRNA modules will be required to design informed strategies for effective engineering of seed traits in sorghum.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Science and Engineering Research Board (SERB), and Department of Science \u0026amp; Technology (DST), Government of India, grant/award numbers: CRG/2019/001695; IFA17-LSP90; PDF/2019/002365; CRG/2020/003466; STR/2022/000013 and Indo‐German Science \u0026amp; Technology Centre (IGSTC), WISER grant/award number: IGST/WISER2023/RS/39/2023‐24/771).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and designing of the study: N.D., R.A.S., Formal analysis: N.D., G.Y., R.J., I.V., A.T.; Funding acquisition: N.D., I.V., R.A.S.; Methodology: R.J., G.Y., I.V., N.D.; Supervision: N.D., M.K.S., R.A.S.; Visualization: N.D., G.Y., R.J., A.M., A.T.; Writing \u0026ndash; original draft: N.D., R.A.S.; Writing \u0026ndash; review \u0026amp; editing: N.D., R.A.S., G.Y., R.J., A.T., M.K.S., I.V. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eND gratefully acknowledges the research grant from the Science and Engineering Research Board (SERB): (CRG/2019/001695) and DST INSPIRE grant IFA17-LSP90 by the Government of India. RAS thanks DST-SERB for CRG/2020/003466 and STR/2022/000013 grants, and Indo-German Science \u0026amp; Technology Centre (IGSTC) for the WISER grant, IGST/WISER2023/RS/39/2023-24/771. RJ thanks financial assistance through an ICMR for the senior research fellowship. GY acknowledges CSIR-UGC for the senior research fellowship (HR0402430044). IV acknowledges the NPDF grant from the SERB, Government of India (PDF/2019/002365). AM thanks the Central University of Haryana, Mahendergarh, for the University Non-NET fellowship. AT thanks DST INSPIRE SHE for the scholarship. The authors are also thankful for the facilities provided by Jawaharlal Nehru University, New Delhi, DST FIST lab (SR/FST/LS-1-2019-471/C), and CIC Central University of Haryana, Mahendergarh.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sequencing data has been deposited to the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) under the BioProject ID: PRJNA1224756.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrews S (2010) FastQC: a quality control tool for high throughput sequence data. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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BMC Plant Biol 9:149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2229-9-149\u003c/span\u003e\u003cspan address=\"10.1186/1471-2229-9-149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Grain size, miRNA, sorghum, seed development, small RNA sequencing, grain filling","lastPublishedDoi":"10.21203/rs.3.rs-7092705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7092705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSorghum is a prominent cereal crop of global importance. Advances in seed research are essential for the enhancement of seed yield and nutritional value in sorghum. Here, we report small RNA profiling from five stages of sorghum seed development depicting miRNA dynamics during pollination, fertilization, early seed development, grain filling, and maturation. We identified 226 miRNAs, 72 of which were known while 154 are novel. Based on the predominant expression patterns, all miRNAs could be classified into five distinct groups. Target prediction unveiled 6640 miRNA-target modules of which 1507 were predicted to regulate grain size. Based on the experimentally verified functions of the orthologs of miRNAs and their targets, 83 modules comprising 16 miRNA families and 24 target genes were shortlisted as high-priority candidates for grain size control. Among these, 13 modules co-localized with previously known grain size quantitative trait loci (QTLs) in sorghum. Furthermore, a total of 12, 5, 3, and 3 candidate modules were implicated in regulating starch content, seed dormancy, seed vigor, and seed shattering, respectively. By integrating the expression profiles of miRNAs and their targets with the comparative genomic data, we could gain global insights into the specific roles of miRNAs in regulating seed development and associated agronomic traits.\u003c/p\u003e","manuscriptTitle":"Small RNA sequencing unveils predominant expression patterns and miRNA-target modules active during seed development in sorghum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 09:32:03","doi":"10.21203/rs.3.rs-7092705/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6d562eb-068b-4e04-9e3a-ea0ec5f96359","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T09:32:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 09:32:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7092705","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7092705","identity":"rs-7092705","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}