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Cold stress‐induced miRNA and degradome changes in four soybean varieties differing in chilling resistance

Cold stress‐induced miRNA and degradome changes in four soybean varieties differing in chilling... INTRODUCTIONSoybean [Glycine max (L.) Merr.] is one of the most important oilseed plants cultivated worldwide, with a production of 347 million metric tonnes in 2017 (https://www.soymeal.org/soy‐meal‐articles/world‐soybean‐production/). It is an exceptional source of oils and proteins used in human nutrition and in animal feed formulation. Additionally, soybean‐derived oils are used in the production of biofuels (Wang & Komatsu, 2018). Symbiosis between soybean and Bradyrhizobium japonicum results in the formation of root nodules (Egamberdieva et al., 2018). The beneficial role of binding of atmospheric nitrogen is a distinctive aspect of soybean cultivation that considerably improves soil composition and helps to reduce the use of fertilizers.An important issue of soybean cultivation in high latitudes is the lack of tolerance to low temperatures, which affects the nodulation process as well as the general growth and yield quality of the plants (Zhang et al., 2014). It has been predicted that in the future, due to climate change, the frosts will become more severe, especially upon seed set (Redden, 2021). Soybean requires relatively high temperatures for germination, growth, development and maturation, and the suitable temperature for soybean is 15–22°C at emergence, 20–25°C at flowering and 15–22°C at maturity (Liu et al., 2018). In temperate climatic conditions, soybeans may be exposed to chilling stress mainly during two periods. The first period is the emergence and early vegetative stages of plant development (V1–V3; Nleya et al., 2019), that is, from the last 10 days of April to mid of May. It has been suggested that the first hours of imbibition are crucial and that low‐temperature–caused injuries are expressed as both reduced emergence of seedlings and reduced vigour and yield of surviving plants (Bedi & Basra, 1993; Skrudlik & Kościelniak, 1996). This issue is of major agricultural importance because germination in cold soil can markedly reduce productivity. The second most sensitive to chilling period is during the flowering phase because pod formation is a process critical for legume crop productivity. Temperatures of 15°C in day and between 15 and 9°C in night are biological minimums at this growth stage (Hume & Jackson, 1981a). The sensitivity of soybean to night temperatures below 15°C is reflected in changes occurring in metabolism, growth, development and yield (Alsajri et al., 2019; van Heerden et al., 2003; Kurosaki et al., 2003; Tyczewska et al., 2016). Exposure to low temperatures causes the accumulation of osmoprotectants such as proline and sucrose as well as reorganization of the membrane structure (Ahmad & Majeti, 2012; Michaelson et al., 2016). Commonly, under chilling conditions, the expression levels of stress‐responsive genes, including many transcription factors and enzymes, are affected (Baillo et al., 2019). Moreover, a single night of cold, with minimum temperatures of 8°C, is sufficient to inhibit pod formation (Hume & Jackson, 1981b). Thus, under natural growth conditions, soybean yield is resilient to cold temperatures that fall to as low as 15°C. However, temperatures below this level pose a significant risk for reducing yield, especially when they fall to 10°C.One of the mechanisms that regulate gene expression in plants relies on microRNAs (miRNAs). These are small noncoding RNAs that interact with genes involved in the regulation of plant growth, development and response to abiotic and biotic stresses (Hume & Jackson, 1981a). These interactions comprise mRNA cleavage based on the binding to complementary sequences within its target genes as well as translation inhibition by restriction of the ribosome binding process (Bartel, 2004). To date, a number of miRNAs in soybean has been reported to be associated with response to abiotic and biotic stresses such as drought, salinity (Liu et al., 2016), phosphate starvation (Xu et al., 2013), cold (Kuczyński et al., 2020) and cyst nematode infection (Li et al., 2012). However, the involvement of miRNAs in the chilling stress response in soybean is poorly explained and needs further investigation.In this study, we compared the responses of four soybean cultivars differing in cold stress tolerance (Augusta, Fiskeby V, Toyomusume and Glycine soja) to chilling stress at the molecular level, with a focus on miRNAs and their target genes. The assessment of cold stress effects in three tissues [shoots (trifoliates), seedling roots and cotyledons] facilitated the comprehensive investigation of stress response in soybean early growth stages. The sequencing of small RNAs isolated from plants cultivated under control and stress conditions allowed to characterize miRNAs involved in the chilling stress responses. Additionally, the sequencing of degradome enabled to identify potential target genes of differentially expressed miRNAs in soybean. This study aimed to determine the responses of the above‐mentioned four soybean cultivars to chilling stress at the molecular level and to elucidate the role of specific miRNAs in soybean tolerance system. Our results provide insights into cold stress response mechanisms and the regulatory roles of miRNAs in cultivated and wild soybean cultivars.MATERIALS AND METHODSPlant materialFour soybean varieties were chosen for the experiment: Augusta, Fiskeby V, Toyomusume and G. soja. Fiskeby V was bred by Dr. Sven A. Holmberg in Sweden, near the city of Norrkoping (58°30′N). Augusta was selected from two crosses: in the first step, a cross was made between Fiskeby V and line PI 194643, and line 104 was obtained; in the second step, line 104 was crossed with line 11 (G. soja wild species). Line 11 of G. soja grows in the natural environment of far Eastern Russia at latitudes similar to those of Poland and has a long‐day–tolerant genotype. Therefore, Augusta has two sources of photoperiod insensitivity, and its chilling tolerance is derived from Fiskeby V. The seeds of the Augusta and Fiskeby V soybean cultivars were provided by Prof. J. Nawracała from the Poznan University of Life Sciences, Poland. The chilling tolerance of the Swedish cultivar Fiskeby V is presumed to be derived from the Sakhalin landrace Namikawa. Glycine soja is a wild soybean annual species that is native to China, Japan, Russia and parts of Korea and is a wild progenitor of the cultivated species G. max. Glycine soja accession PI 538411A was collected over Amur River (Far East of Russia) on latitude: 52°58′39″N and longitude: 127°21′44″E. Toyomusume was chosen as a chilling‐sensitive genotype. It is a Japanese variety from Hokkaido Island, where it is cultivated mainly for tofu production.Prior to sowing, the soybean seeds were inoculated with B. japonicum (HiStick® Soy, BASF) to induce nodule formation. The soybean varieties Augusta, Fiskeby V, Toyomusume and G. soja were planted in pots filled with a mixture of all‐purpose potting soil and sand in the ratio of 3:1. Plants were grown under controlled environmental conditions in a phytotron at 20°C with a relative humidity of 60% and a 16:8‐h light: dark photoperiod prior to stressing treatment.The plants were divided into two groups, with each group subjected to chilling treatment at a different developmental stage (Table 1). The first batch of plants was stressed at the VE stage (emerging seedlings) by keeping them at 4°C for 48 h in Percival chambers. The next batch of plants was exposed to 8°C for 120 h (5 days) at the V1 growth stage (first trifoliate). In the control and treated groups, 20 to 30 soybean plants were cultivated.1TABLEScheme of the chilling stress treatmentStage of soybean growthOptimal growth temperature (°C)Stress temperature (°C)Duration of stress conditions (h)VE – seedlings20448V1 – vegetative208120sRNA library construction and sequencingTotal RNA was isolated from the trifoliates, seedling roots and shoots of the four soybean genotypes by using Direct‐zol™ RNA Miniprep Plus (Zymo Research) according to the manufacturer's instructions. The quantity and purity of the total RNA were checked using NanoDrop ND‐1000 (NanoDrop) and Agilent 2100 with a minimum RNA integrity number (RIN) threshold value of >8.0. Approximately 5–10 µg of total RNA was used for the sRNA library construction according to the protocol of TruSeq Small RNA Sample Prep Kits (Illumina). A total of 72 soybean sRNA libraries were constructed, from both chilled and control seedling roots and cotyledons as well as V1 stage shoots, all performed in triplicate. The libraries were sequenced on an Illumina Hiseq4000 at BGI Tech Solutions Co., Ltd.Bioinformatics analysis of sequencing dataThe sequencing data were filtered to contain only fragments of at least 18 nt in size. The sequences were then further processed to contain at least five counts for each variety/tissue combination. The raw counts were subsequently analysed using edgeR Bioconductor package in R following the developer's instructions (Robinson et al., 2009).The known miRNA sequences were identified using BLAST against all miRBase mature sequences downloaded in March 2019. Only sequences identical to the reference miRNAs were retained for further analyses. Novel miRNAs were searched using the miRDeepP2 program (Kuang et al., 2018) with default options and using G. max reference genome version 2.1 downloaded from ENSEMBL in May 2019. Only sequences that did not map to the reference miRBase dataset were retained for further analyses.ddPCR analysisThe profiles of four differentially expressed miRNAs were assayed by ddPCR. A set amount of extracted RNA (1 µg sRNA and 1.5 µg of total RNA) was reverse transcribed using SuperScript IV Reverse Transcriptase (Thermo Fisher Scientific) as described in Varkonyi‐Gasic et al. (2007). Stem‐loop primers were designed for the miRNA reverse transcription reactions. The RT‐PCR primers are listed in Table S1. To quantify the number of miRNA molecules in the plant samples, a ddPCR mixture containing 10 µl of ddPCR SuperMix Eva Green (BioRad), primers (the final concentration of each primer was 200 nM), template (reverse‐transcribed, elongated miRNA) and RNase‐free H2O was used. A 20 µl reaction mixture was used to generate the droplets in an eight‐well cartridge by using a QX100 droplet generator (Bio‐Rad). The droplets were carefully transferred to a 96‐well ddPCR plate and heat‐sealed with a foil (Bio‐Rad). The cDNA was then amplified in a T100 PCR thermal cycler (Bio‐Rad) under the following cycling conditions: 5 min of denaturation at 95°C, followed by 40 cycles with a three‐step thermal profile of 30 s of denaturation at 95°C, 30 s of annealing at 55°C and 45 s of extension at 72°C. Subsequently, the products were maintained at 72°C for 2 min for final extension. After amplification, the products were cooled to 4°C for 5 min, heated to 90°C for 5 min and finally cooled again to 12°C. The droplets were quantified in a QX100 droplet reader (Bio‐Rad). Data acquisition and analysis were performed using QuantaSoft software (Bio‐Rad). The positive droplets containing the amplification products were differentiated from the negative droplets by setting the fluorescence amplitude threshold to the lowest value of the positive droplet cluster. Yeast tRNA‐Thr(TGT) molecules with a scrambled sequence were added to each RT reaction mixture as an internal control.Degradome sequencingThe quantity and purity of the total RNA were checked using NanoDrop ND‐1000 (NanoDrop) and Agilent 2100 with a minimum RIN threshold value of >7.0. Approximately 20 µg of total RNA was used to prepare 24 degradome libraries, with three biological replicates for both stress and control conditions of the four cultivars. The mRNA was mixed with biotinylated random primers, and the RNA containing the biotinylated random primers was then captured by beads and ligated to adaptors, according to the BGI Tech Solutions Co., Ltd., procedure. Following purification, digestion, ligation and repurification, the cDNA library was sequenced (single end, 36 bp) with an Illumina Hiseq4000 at BGI Tech Solutions Co., Ltd. Clean reads of the degradome sequencing were obtained by removing low‐quality reads and the adapter contaminants. The clean reads of the degradome data were then mapped to the soybean (G. max) genome. CleaveLand pipeline 4.3 (https://sites.psu.edu/axtell/software/cleaveland4/) was used to confirm the potential miRNA: target gene pairs with default parameters.The putative targets for the selected sRNA reads were identified using CleaveLand4‐4.5 (Addo‐Quaye et al., 2009) program according to the developer's instructions. Prior to the analysis, the degradome data were adapter and quality trimmed using the cutadapt tool (Martin, 2011). mRNA sequences downloaded from the NCBI RefSeq database in June 2019 were used as a reference for miRNA target detection.Gene expression analysis using real‐time PCRTotal RNA was isolated from frozen powdered samples (100 mg) using NucleoSpin RNA Plant (Macherey‐Nagel) following the manufacturer's protocol. Total RNA (2 µg) was used for cDNA synthesis with the Transcriptor First‐Strand cDNA Synthesis Kit (Roche) using oligo (dT) primers according to the manufacturer's instructions. For real‐time PCR analysis, the cDNA template was diluted two times. Real‐time PCR analysis was performed to determine the expression levels of the Glycine max putative phytocyanin (Phyt), Glycine max transcriptional factor NAC‐19 (NAC‐19) and Glycine max malate dehydrogenase [NADP] (GmMDH), and each cDNA sample was analysed using Mono Color Hydrolysis UPL Probes (Roche) selected for each gene by using ProbeFinder Software (Roche). The PCR reaction mixtures were prepared according to the manufacturer's protocol. PCR conditions were as follows: initial incubation step at 94°C for 10 min, followed by 45 cycles of amplification (15 s at 94°C, 30 s at 60°C and 15 s at 72°C; single acquisition), with a final cooling step at 40°C for 2 min. The analysis was performed using a LightCycler 480 II instrument (Roche). Relative gene expression was calculated using the Roche Applied Science E‐Method and normalized to the three reference genes (Glycine max actin‐6 (ACT‐6), SKP1/Ask‐Interacting Protein 16 (SKIP16) and Eukaryotic elongation factor 1 β (ELF1B; Hu et al., 2009). All standard curves were generated by amplifying a series of twofold dilutions of cDNA. The primer sequences for the analysed genes and UPL probes are given in Table 2.2TABLEList of primers and UPL probes used for real‐time PCR analysisGene shortcutFull gene nameForward primer (5′−3′)Reverse primer (5′−3′)UPL no.ACT‐6 aGlycine max actin‐6CAGGAATGGTTAAGGCTGGTCGAGGACGACCTACAATGCT67SKIP16 aSKP1/Ask‐Interacting Protein 16GGGATGGGATGGGATAGAATTCCCAAATAATGAAATTGAGACTTC31ELF1B aEukaryotic elongation factor 1 βAGTTTTGTTTTTCTTTATTTGAATTGCCAGCGCCACTGAATCTTACC72PhytGlycine max putative phytocyaninGCACCACAGGCAATTCACTAGGCAGTCTTGAGGGTGATTG59NAC20Glycine max transcriptional factor NAC‐19CGAAATGGAAGACGTGAAGCTCGGAAGCCTCGAAGTACAG70GmMDHGlycine max malate dehydrogenase [NADP]AAGATGCAGAATGGGCTTTGGCCCATTTATGTCTAACAAGTCTG85aReference genes are marked in bold.GO analysisGO functional classification was performed using Blast2GO (https://www.blast2go.com/) with the default settings. The sequences were blasted using NCBI BLAST service (QBlast) and a blastx‐fast program. Blast expectation value (E‐value) was set at 1.0E−3. Next, the GO ontology mapping and InterProScan were performed. GO mapping was performed against extensively curated gene ontology annotated proteins to obtain functional labels. The used data originate from the Gene Ontology Association and Uniprot ID‐Mapping. The public EMBL‐EBI InterPro web service was used to scan sequences against InterPro's signatures with the default settings. Go annotation was performed with the annotation cut‐off set at 55 and E‐value hit filter set at 1.0E−6. Enrichment analysis was performed using Fisher's exact test.RESULTSAnalysis of small RNA sequencing librariesTo identify the miRNAs involved in the chilling stress response, 72 sRNA libraries were constructed for cold‐stressed samples, and their corresponding controls that were sequenced by Illumina Technology. The number of raw sequencing reads per sample ranged from 20.2 to 43 million (Table 3).3TABLEStatistics of soybean sRNA sequencing librariesSampleTotal raw readsUnique raw readsTotal filtered readsUnique filtered readsARC31748684467279825733227348281ARS27488230404280322482467340208,7FRC35274590575501927375919421643,3FRS33272581461187227384545402207,3GRC31371582452014425182469311193GRS32342809393441727081801294042,7TRC34799529507382727851481408338,3TRS32134038435888626684040394515,3ACC34831334388114428492562458136,7ACS32385665364430226485865438347,7FCC25825875220820922059012289776,7FCS26971906218295523513888277404,7GCC32915940440745426261721392516GCS31400723356112226232399362572,3TCC30884146279010225515349348449,3TCS34427857317006229265725369728,3ASC33943160381612127873908456142,3ASS31835213374930426172147453468,7FSC31749968387572225430403424815,3FSS32928969286875328472060382114GSC36552754468699729270721423320GSS30993728417783924756494413838,3TSC31087038344866925297087420317TSS36333521326983629412734406057,7aFirst letter of the sample name designates cultivar: A – Augusta, F – Fiskeby V, G – Glycine soja, T – Toyomusume; second letter designates tissue: R – root, C – cotyledon, S – shoot; third letter designates conditions: C – control, S – stress.After removal of the adapter sequences and low‐quality reads, the total read count ranging from 16.6 to 35.9 million and the unique read count ranging from 240 to 525 thousands were obtained from these 72 libraries (Table 3). Quantification of miRNAs between the cultivars and treatments was preceded by the normalization of expression levels of the miRNA families to counts per million (cpm). The normalized miRNA families read frequencies ranged from 1 to 552,267 cpm for the trifoliates of V1 stage, from 1 to 134,679 cpm for the root samples and from 1 to 165,310 cpm for the cotyledon samples (Table 3). Size distributions based on the filtered reads were assessed and are shown in Figure 1. The majority of mapped sequences were found to be 21 nt long (18.1%), followed by sequences that were 20 and 22 nt long (12.7% and 12.3% respectively).1FIGURESequence length distribution of miRNAs in sRNA libraries of soybeanIdentification of known and novel miRNAs in soybeanThe filtered reads from 72 sRNA libraries, from both chilled and control seedling roots and cotyledons as well as V1 stage shoots, all performed in triplicate, were compared with the collection of mature (and precursor) soybean miRNAs from miRBase release 22.1. A total of 321 known soybean miRNA families were identified in all four cultivars (Table S2). In cotyledons of soybean at the VE stage, the most abundant miRNA family was miR159, except for Fiskeby V in which miR165 was the most highly expressed miRNA. In roots (radicles) of all plants collected at the seedling (VE) stage, the most extensively expressed miRNA family was miR319. In trifoliates of soybean collected at the V1 stage, the most abundant miRNA family was miR398. Additionally, among the highly expressed miRNA families in all the studied samples, there were mostly conserved miRNAs such as miR159, miR165, miR166, miR167, miR319, miR396, miR398, miR408 and miR482 and legume‐specific miRNAs such as miR4414, miR1510 and miR3522. On the other hand, among the least expressed miRNA families, the ratio of conserved miRNAs to legume‐specific miRNAs was substantially lower than that in highly expressed fraction. Most of the conserved miRNAs among the least abundant miRNA families were transcriptional variants of their highly expressed counterparts.Additionally, 348 novel miRNAs were found in the four studied cultivars (213 miRNAs in Augusta, 220 in Fiskeby V, 224 in Toyomusume and 218 in G. soja) (Table S3), among which 129 miRNAs were common for all four cultivars.Differential expression of miRNAs in soybeanTo identify the miRNAs that exhibit differential expression patterns under chilling stress, we used fold‐change values of 1 and −1 and p‐value of .05 as the threshold. A total of 162 differentially expressed miRNAs belonging to 137 miRNA families were identified between chilled and control samples of the four soybean cultivars. The distribution of differentially expressed miRNAs in shoots, cotyledons and roots of all studied varieties is given in Figure 2.2FIGUREQuantitative distribution of differentially expressed miRNAs in shoots, cotyledons and roots of the studied cultivars; S – shoots, R – roots, C – cotyledonsAmong these differentially expressed miRNAs in all the tested cultivars, 93 were downregulated in chilled samples as compared to those in controls in at least one cultivar, 28 of which were downregulated by at least fivefold. MA plots of differentially expressed miRNAs in four studied cultivars are given in Figure 3.3FIGUREMA plots of differentially expressed miRNAs in four studied cultivars; red dots – upregulated, blue dots – downregulatedOn the other hand, 137 miRNAs were upregulated in samples from stressed plants compared to those in controls in at least one cultivar, 41 of which were upregulated by at least fivefold (Figure 3). The most prominent reduction in expression during chilling stress was noted for miR169, miR166, miR159 and miR5037 in cotyledons (VE stage); for miR159, miR160, and miR171 in seedling roots (VE stage) and for miR167, miR2111, miR5371, miR5037, miR398, miR4416 and miR160 in trifoliates from V1 stage. Conversely, the most remarkable increase in the expression levels was observed for miR6300, miR5368, miR6173 and miR1509 in cotyledons (VE stage); for miR319, miR9750, miR408 and miR2109 in seedling roots (VE stage) and for miR166, miR398, miR2119, miR399, miR4996, miR171, miR6300, miR5368, and miR169 in the trifoliates from V1 stage among the four cultivars. Several miRNA families, including miR159, miR319 and miR482, were differentially expressed universally in seedling roots, with miR159 and miR482 being downregulated in all the four cultivars, whereas miR319 was downregulated in Augusta and G. soja and upregulated in Fiskeby V and Toyomusume. Similarly, in the trifoliates of plants stressed at V1 stage, there were miRNAs differentially expressed in all the studied soybean cultivars, including miR10197, miR1507, miR1509, miR159, miR166, miR2111, miR3522, miR396, miR398, miR408 and miR4996, where miR10197 and miR2111 were downregulated, miR1509 and miR396 were upregulated in all cultivars, while miR1507, miR159, miR166, miR3522, miR398, miR408 and miR4996 were downregulated in Augusta and G. soja and upregulated in Fiskeby V and Toyomusume (Figure 4).4FIGUREHeatmap of differentially expressed miRNAs in trifoliates of four studied soybean cultivarsRegarding the differences in expressional patterns between the four cultivars, several notable miRNAs showed contrasting expression in the chilling‐sensitive cultivar Toyomusume and the chilling‐tolerant cultivars Augusta, Fiskeby V and G. soja. In the seedling roots of Augusta, Fiskeby V and G. soja, miR169 and miR5770 were found to be downregulated by 2.3‐, 3.3‐ and 2.4‐fold and by 2.9‐, 1.7‐ and 2.1‐fold, respectively, while their expression was not significantly altered in Toyomusume, thus suggesting their involvement in the chilling stress response (Figure 5). Similar patterns were found in trifoliates (V1 stage) for miR156 and miR5770, where they were downregulated by 1.7‐, 3.9‐ and 1.9‐fold and by 2.1‐, 3.6‐ and 2.3‐fold in Augusta, Fiskeby V and G. soja, respectively, while their expression was again not altered significantly in Toyomusume. Interestingly, there were seven miRNAs (miR1507, miR159, miR166, miR 3522, miR398, miR408 and miR4996) with common expression trend of upregulation in Toyomusume and Fiskeby V, while being downregulated in Augusta and G. soja.5FIGUREHeatmap of differentially expressed miRNAs in roots of four studied soybean cultivarsAdditionally, 18 novel miRNAs were differentially expressed between stressed and control samples. Among them, four miRNAs were found in more than one sample. Two of them were common for cotyledons and trifoliates of G. soja, one was shared by trifoliates of G. soja and Augusta and one by trifoliates of G. soja and Fiskeby V.Validation of miRNA expression by ddPCRTo verify the reliability of sequencing results, the expression levels of four miRNAs (miR169, miR408, miR2109 and miR5770) responsive to chilling stress were evaluated by ddPCR (Figure 6). The ddPCR results showed that the expression patterns of four selected miRNAs were mostly in accordance with the assessment of sRNA sequencing (except for miR169 in Augusta and Fiskeby V, and miR408 in Toyomusume). The discrepancies in the fold change of particular samples can be attributed to the differences in the sensitivity and specificity of the two techniques. Moreover, in ddPCR, it is difficult to differentiate between the particular miRNAs belonging to one miRNA family, which may further explain some differences observed between sequencing and ddPCR results.6FIGUREValidation of expression of four miRNAs at two developmental stages of four soybean cultivars. AS – Augusta roots, FR – Fiskeby V roots, TR – Toyomusume roots, GR – G. soja roots, AS – Augusta seedlings, FS – Fiskeby V seedlings, TS – Toyomusume seedlings, GS – G. soja seedlingsmiRNA target profiling by degradome sequencingOn the basis of the degradome sequencing and subsequent CleaveLand program analysis, we identified potential targets of soybean miRNAs that are involved in the chilling stress responses. In total, 2005 targets were identified in all 24 libraries, among which 1183 belonged to Category 0, 222 to Category 1, 293 to Category 2, 164 to Category 3 and 143 to Category 4, where Category 0 represented the best match between miRNA and target genes (Table S4). Further investigation revealed potential targets of differentially expressed miRNAs (Table 4). Examples of miRNAs alignments and their T‐plots validated by degradome sequencing are given in Figure S1.4TABLETarget genes of some differentially expressed miRNAs involved in soybean chilling stress responsemiRNA IDTranscriptTranscript annotationCleavage siteDegradome categoryDegradome p‐valuegma‐MIR1507a+|gso‐MIR1507a+|vun‐MIR1507a+|vun‐MIR1507b+XM_003538342.4PREDICTED: Glycine max aspartic proteinase PCS1 (LOC100803089)2772.038714958gma‐MIR166r+NM_001250783.3Glycine max GAMYB‐binding protein (LOC732608)17433.091742104crt‐MIR166b+|csi‐MIR166f+|gma‐MIR166r+XM_003524945.4PREDICTED: Glycine max homeobox‐leucine zipper protein ATHB‐14‐like (LOC100806322)10550.0012395gma‐MIR3522+|gso‐MIR3522a+|gso‐MIR3522b+NM_001248914.2Glycine max zinc finger protein CONSTANS‐LIKE 2 (COL2B)10170.03432688gso‐MIR3522a+|gso‐MIR3522b+NM_001248512.2Glycine max Cu/Zn superoxide dismutase copper chaperone (CCS)7410.02167003gma‐MIR397a+NM_001354136.1Glycine max NAC transcription factor (LOC100814504)6173.026758707fve‐MIR397+|gma‐MIR397b+|lja‐MIR397+XM_003529084.3PREDICTED: Glycine max laccase‐7 (LOC100814926)7410.002099926MIR408‐|ath‐MIR408+|gma‐MIR408a+|gma‐MIR408b+|gma‐MIR408c+|gma‐ MIR408+|zma‐NM_001255827.1Glycine max NAC domain protein (NAC19)7260.004468gma‐MIR5770a+NM_001358538.1Glycine max putative copper amine oxidase (LOC100819571)2282.019549gma‐MIR482b+|gma‐MIR482d+|gma‐MIR482e+|gso‐MIR482a+|gso‐MIR482b+NM_001254425.2Glycine max TIM21‐like protein, mitochondrial‐like (LOC100779212)2440.028829MIR398b+|gma‐MIR398c+|gma‐NM_001248369.1Glycine max superoxide dismutase [Cu‐Zn] (SOD1)220.002981MIR398b+|gma‐MIR398c+|gma‐NM_001248512.2Glycine max Cu/Zn superoxide dismutase copper chaperone (CCS)7403.069627In seedling roots, miR159 targeted cytochrome P450 and pyruvate dehydrogenase (E1 component subunit alpha‐3), while miR319 targeted TCP transcription, a protein from flavin‐binding monooxygenase family, and cold‐regulated protein (COR1; in G. soja). One of the most represented targets of miR482 was TIM21‐like protein. Regarding the miRNAs and their targets in trifoliates of V1 stage, miR1507 was assigned to aspartic proteinase PCS1 and NBS‐LRR disease resistance protein; miR156 was assigned to scarecrow‐like protein 28, squamosa promoter binding‐like protein (in Augusta) and FAD synthase (in G. soja); miR159 targeted CDPK‐related kinase and pyruvate dehydrogenase (E1 component subunit alpha‐3); miR166 targeted GAMYB transcription factor and homeobox‐leucine zipper protein ATHB‐14 like; miR2111 was found to cleave VSPA (vegetative storage protein A); miR3522 targeted COL2a (CONSTANS‐like 2a); miR398 affected the expression of SOD (superoxide dismutase [Cu‐Zn]) and CCS (Cu/Zn superoxide dismutase copper chaperone) (except for Toyomusume); miR408 was considered to alter the expression of HMA8 (chloroplast copper‐translocating HMA8 P‐ATPase) and NAC domain protein; and miR4996 targeted cysteine proteinase, polyphenol oxidase and omega‐6 fatty acid desaturase (FAD).Gene ontology terms were assigned to 378 target genes controlled by 16 differentially expressed miRNAs (Table S5). Target genes were described by 53 terms in biological process category, 31 terms in molecular function category and 25 terms in cellular compartment category. Highly represented terms included ‘biosynthetic process’, ‘cellular nitrogen compound metabolic process’ and ‘cellular protein modification process’ in biological process category; ‘ion binding’, ‘DNA binding’ and ‘oxidoreductase activity’ in molecular function category; and ‘cell nucleus’, ‘protein containing complex’ and ‘membrane’ in cellular compartment category (Figure 7). Moreover, according to the KEGG analysis, 378 target genes were significantly enriched in 67 pathways, of which the largest number of enzymes affected by the cold stress were those related to sugar metabolism: starch and sucrose metabolism (6), glycolysis/gluconeogenesis (5), galactose (5), amino sugar and nucleo sugar (5) and pyruvate (8) metabolism, pentose phosphate pathway (3) and pentose and glucuronate interconversion (3). Additionally, enzymes involved in carbon fixation in photosynthetic organisms (5), photosynthesis (1) and nicotinate and nicotinamide metabolism (1) were affected after cold treatment in soybean. Glutathione metabolism (3) was also altered due to chilling. The metabolism of various amino acids (including tyrosine, phenylalanine, glycine, serine, threonine, arginine and proline), purine and biotin, isoquinoline alkaloid biosynthesis as well as pyruvate and thiamine metabolism were also indicated as pathways influenced by cold stress (Table S6).7FIGUREGene ontology (GO) analysis of target genes of differentially expressed miRNAsGene expression level analysis using real‐time PCRThree genes were selected for the analysis of changes in gene expression level under chilling in roots and cotyledons of four tested soybean varieties: Glycine max putative phytocyanin (Phyt, NM_001251440.2), Glycine max transcriptional factor (NAC‐19, NM_001255827.1) and Glycine max malate dehydrogenase [NADP] (GmMDH, NM_001369219.1). In roots, following stress exposure, Phyt was upregulated in Augusta and Fiskeby V, contrary to Toyomusume and G. soja, where a significant downregulation of the gene's expression level was observed. NAC‐19 transcription factor was highly upregulated in roots in all tested varieties. GmMDH in roots was upregulated in Augusta, Fiskeby V and Toyomusume, 50% downregulation was noted for G. soja. In cotyledons, the expression of Phyt was downregulated (by at least 50%) in all varieties except for G. soja, where it was upregulated (by 40%). NAC‐19 transcription factor was upregulated in cotyledons of all tested varieties, and GmMDH was upregulated in Augusta and Toyomusume and downregulated in Fiskeby V and G. soja. The graphs presenting changes in the expression levels of chosen genes are presented in Figure 8.8FIGUREThe relative gene expression levels (Y axis) of selected genes (Phyt, NAC‐19 and GmMDH) in soybean varieties under chilling. (a) In roots, (b) in cotyledons. The results are presented as the mean ± SD, from two independent experimental and three technical repeats (treated vs. control). First letter of the sample name designates cultivar: A – Augusta, F – Fiskeby V, G – Glycine soja, T – Toyomusume; second letter designates tissue: R – root, C – cotyledon; third letter designates conditions: C – control, S – stressDISCUSSIONLow‐temperature conditions are one of the critical factors that influence plant growth, development and geographical distribution (Megha et al., 2018). Cold injury can affect plants in several ways, such as by causing disruption of energy generation by blocking photosynthesis, generating systemic oxidative stress caused by excessive production of reactive oxygen species (ROS) and disrupting membrane transport associated with reduced fluidity of the plasma membrane structure (Miura & Furumoto, 2013). miRNAs are crucial players in the regulation of various stress responses and constitute a major part of sequence‐specific gene silencing machinery (Kumar, 2014). Nevertheless, the involvement of miRNAs in the chilling stress response in soybean needs further investigation. The present study aimed to identify miRNAs that participate in soybean's response to chilling stress. To achieve our objective, we used four cultivars (Augusta, Fiskeby V, Toyomusume and G. soja) that differ in their sensitivity to chilling stress. In addition to the traditional comparison of treated and control groups, Toyomusume, as the cultivar susceptible to low temperatures, served as the background in the analysis of the differential expression patterns of stress‐responsive miRNAs. On the basis of the comparison of the aforementioned cultivars and their tissues sampled at two soybean developmental stages, we concluded that miRNAs play an important role in soybean's chilling stress tolerance mechanism. Additionally, we identified 321 known miRNAs along with 348 novel miRNAs in 72 libraries from the four tested soybean cultivars.miRNA in chilling stress responses in soybeanOur approach based on the use of high‐throughput methods broadens the knowledge of mode of action of miRNAs in soybean plants. A previous study showed that cold stress influences the growth of two tested varieties, namely Augusta and Fiskeby V, which was observed as phenotypic changes (Kuczyński et al., 2020). It was also shown that under chilling stress, the expression levels of several miRNAs (miR169, miR319, miR397 and miR398) and their target genes had changed. In the present study, amid the plethora of miRNAs that exhibited differential expression between the four studied cultivars, a group of miRNAs with a common expression pattern in all the analysed varieties caught our immediate attention. This group consisted of both conserved miRNAs (miR159, miR2111, miR396 and miR482) and legume‐specific miRNAs (miR10197 and miR1509). Among these, miR1509 and miR396 were found to be upregulated in trifoliates of chilled soybean plants at V1 stage, whereas miR10197, miR159, miR2111 and miR482 were downregulated. In the analysis of the degradome libraries, genes that may be the targets of these miRNAs were identified. These genes included pyruvate dehydrogenase, auxin signalling F‐box 2, aspartyl protease, TIM21‐like protein, TMV resistance protein N and vegetative storage protein. Such expression trends of these miRNAs suggest that their target genes play a universal role in controlling chilling stress response across different soybean cultivars. Most of the differentially expressed miRNAs reported in this study were found in the trifoliates. Expression profiles of some miRNAs showed specificity towards a particular tissue, in that they were found only in radicles, such as miR10190 and miR862, or in trifoliates, such as miR1511, miR168 and miR391. Interestingly, expressional patterns of other miRNAs such as miR1509, miR10440 or miR171 proved that chilling stress can cause one miRNA to be upregulated in one tissue but to be downregulated in another tissue. Differential expression of miRNA in various parts of soybean plants has been reported previously (Sun et al., 2016). Additionally, findings detailing the expression of miR157 in leaves and roots of Prunus persica (Eldem et al., 2012) support the existence of this phenomenon.Thirteen miRNAs were found to have contrasting trends of expression in Toyomusume and at least two other cultivars. These trends were observed mainly in trifoliates (V1 stage); however, the differential expression of most of these miRNAs was detected in all tissues in at least one cultivar. miR156 was found to be downregulated in Augusta, Fiskeby V and G. soja, whereas no significant change in expression was observed in Toyomusume. Interestingly, in all other cases, Fiskeby V and Toyomusume shared the expressional pattern of upregulation, as opposed to Augusta and G. soja, in which the said miRNAs were downregulated during chilling stress. An exception to this tendency was miR319, whose levels increased in Augusta and G. soja and decreased in Fiskeby V and Toyomusume under low‐temperature conditions. The legume‐specific miR1507, which was downregulated in trifoliates of Augusta and G. soja and upregulated in Fiskeby V and Toyomusume, was predicted to control the expression of aspartic proteinase, which plays a role in protein turnover and biotic stress tolerance in plants (Mazorra‐Manzano & Yada, 2008). Furthermore, two soybean‐specific miRNAs, namely miR3522 and miR4996, exhibited analogical expression patterns to miR1507. miR3522 was assigned to the CONSTANS‐LIKE 2B/A, which is involved in developmental processes, including flowering and root elongation (Steinbach, 2019). According to the degradome analysis, miR4996 potentially cleaves the transcripts of cysteine proteinase, which is responsible for the degradation of proteins from energetic reserves and proteins damaged due to stress conditions (Grudkowska & Zagdańska, 2004), and omega‐6 FAD, which regulates the content of unsaturated fatty acids in the plasma membrane (Dar et al., 2017). Furthermore, many genes predicted to be targeted by differentially expressed miRNAs in this study remain uncharacterized, which leaves much room for future advancement in elucidating chilling stress response in soybean.Annotation of differentially expressed conserved miRNAs with their putative target genesConversely, the annotation of differentially expressed conserved miRNAs with their putative target genes proved to be more fruitful (compared to legume/soybean‐specific miRNAs). miR156, which was downregulated in trifoliates of Augusta, Fiskeby V and G. soja but upregulated (not significantly) in Toyomusume, was predicted to control the expression of teosinte glume architecture 1 (TGA1), the transcriptional regulator belonging to the SBP (squamosa promoter binding‐like protein) family, which was found to play a role in transition from juvenile to adult stage in maize, where it was also shown to be targeted by miR156 (Studer et al., 2017). miR159, which was downregulated in trifoliates of Augusta and G. soja but upregulated in Fiskeby V and Toyomusume, was predicted to target the CDPK‐related kinase 6 (CRK6), which is reported to be involved in ROS metabolism and shown to have an extensive role in the abiotic stress tolerance response in Oryza sativa, Zea mays, Populus trichocarpa and Brassica napus (Bulgakov et al., 2011; Xiao et al., 2017). According to the study conducted on the vegetable soybean, the expression of miR159 was decreased due to chilling stress (Xu et al., 2016). miR166, having the same expressional pattern as miR159, was proposed to regulate ATHB‐14, ATHB‐15 and GAMYB. Another research group also reported ATHB‐14 as a target gene of miR166 in soybean (Li et al., 2017). In that study, the authors found members of the miR166 family to be responsive to cold stress (Li et al., 2017). In Camellia sinensis, several members of the miR166 family were downregulated under drought stress, and a negative correlation was observed between the expression of miR166 and ATHB‐14 like and ATHB‐ like (Guo et al., 2017). miR319 is known to target transcription factors from the family of TCP (TEOSINTE BRANCHED1/CYCLOIDEA/PCF) involved in leaf morphogenesis (Bresso et al., 2018). Our results showed that during chilling stress, miR319 was downregulated in roots of Fiskeby V and G. soja, with opposite tendencies in Toyomusume and Augusta. Furthermore, TCP2/3/4 were assigned as target genes of miR319 in the degradome analysis, which was corroborated by the work of another research group that described the relationship of miR319 and TCP3/4 in the context of flavonoid biosynthesis in soybean (Gupta et al., 2019). Another pattern of downregulation in Augusta and G. soja and upregulation in Fiskeby V and Toyomusume (not significantly) was observed for miR397 in trifoliates. Other authors reported that during water deficit in soybean, miR397ab showed downregulation in a resistant cultivar but upregulation in a sensitive cultivar (Kulcheski et al., 2011). Here, we predicted that NAC18/19 (Petunia No Apical Meristem (NAM), Arabidopsis transcription activation factors (ATAF1 and ATAF2), cup‐shaped cotyledon 2 (CUC2)) and laccase‐7 may be potential targets of miR397. NAC is a family of transcription factors involved in various developmental processes such as hormone signalling, fruit ripening and stress response (Hussain et al., 2017). According to Yang et al. (2019) NAC109 was upregulated in roots and shoots shortly after cold stress in soybean. Another research group reported that the expression of NAC19 was induced by ABA and JA, and it was engaged in the process of programmed cell death accompanied by ROS accumulation (Wang et al., 2015). Laccases in plants are mostly associated with the lignification process; however, they were also found to be involved in abiotic stress response in Arabidopsis (Wang et al., 2019). ROS are almost an inseparable factor of abiotic stress in plants; therefore, the capacity of any given organism to neutralize these pernicious molecules is essential for endurance of adverse conditions (Dar et al., 2017). Nature's response to ROS is the development of the antioxidative system, including copper/zinc SOD (Feng et al., 2016). Our data showed downregulation of miR398 in trifoliates of Augusta and G. soja, and the opposite expression pattern was observed in Fiskeby V and Toyomusume. Further analysis suggests that Cu/Zn SOD and CSS are the target genes of miR398. Previous studies have shown that miR398 downregulates the transcription of its target genes, namely CCS and Cu/Zn SODs (CSD1 and CSD2), in Arabidopsis, thus corroborating its role in stress regulation (Beauclair et al., 2010; Guan et al., 2013).Novel miRNA with differential expression under chilling in soybean and their target genesIn the present study, 18 novel miRNAs were found that exhibited significant differential expression between control and chilled samples. The majority of these miRNAs were identified in cotyledons and trifoliates of G. soja. This may be explained by G. soja having the largest difference in its genome as compared to other tested cultivars that belong to the genus Glycine. Further analysis enabled to classify the target genes for some of the novel miRNAs found responsive to chilling stress. For example, novel miR151035 targeted the scarecrow‐like (SCL6) protein in trifoliates of Fiskeby V. In Arabidopsis, SCL6 was found to coordinate the shoot branching process (Wang et al., 2010). Another study showed that SCL6 is involved in the nodulation process in soybean (Hossain et al., 2019). Novel miR119406 targeted ATP sulphurylase (ATPS) in trifoliates of G. soja. The expression of ATPS was observed to be induced due to cold treatment in soybean (Phartiyal et al., 2006). ATPS, as a crucial enzyme in the sulphur assimilation pathway, controls the rate of cysteine synthesis, which is one of the substrates of glutathione that plays a role in cold resistance of plants (Phartiyal et al., 2006). Interestingly, novel miR60377/64371/49212 targeted a protein containing F‐box/kelch repeat in trifoliates of G. soja and Augusta as well as in cotyledons of G. soja. F‐box proteins are a part of the E3 ubiquitin–protein ligase complex and were reported to be involved in the response to salinity, drought and heavy metal stress in Medicago truncatula (Song et al., 2015).Target gene expression analysis under chilling in soybeanThe analysis of expression levels of selected genes showed changes caused by cold stress; one of the genes is transcription factor NAC‐19 which belongs to one of the largest TF families that regulate plant growth, development and responses to environmental stresses (Diao et al., 2020; Zhang et al., 2018). In the present study, following chilling stress, NAC‐19 was significantly upregulated in roots and cotyledons of all tested varieties. This result correlated well with observed downregulation of miR408 in cotyledons which was predicted to target NAC‐19 (Table 4; Figure 6). To date, several reports indicated upregulation of many NAC TFs by cold stress in several different plant species like A. thaliana, Brassica napus, Capsicum annum L., Glycine max, Oryza sativa, Triticum aestivum, Zea maize and many more (reviewed in Diao et al., 2020).NADP dehydrogenases are key components of NADPH production systems necessary to maintain redox balance in the cells, and preserving redox homeostasis is especially important during stress exposure (Begara‐Morales et al., 2019; Sun et al., 2019; Wang et al., 2016). Malate dehydrogenase (GmMDH) belongs to a group of oxidoreductases that catalyse the conversion of malate and oxaloacetate, the reaction accompanied by reduction in the NAD(H) or NADP(H) pool. NADP is an important reducing agent for the synthesis of defensive substances and anabolic reactions (Sun et al., 2019; Wang et al., 2016). The role of MDH in stress responses has been proven, among others, in A. thaliana (Hebbelmann et al., 2012; Zhao et al., 2020), transgenic apple plants (Wang et al., 2016) and winter rye (Crecelius et al., 2003). Hence, it is not surprising that in roots of Augusta and Toyomusume varieties, GmMDH levels were upregulated, or maintained at the same level (in Fiskeby V). Only in one variety (G. soja), a decrease in the levels of GmMDH was observed. In cotyledons only, Augusta maintained increased expression level of GmMDH. An increase was also observed in G. soja. Phytocyanins (PCs) are ancient blue copper proteins which function as electron transporters. Previously it has been shown that PCs play important roles in cell differentiation and reorganization, organ development and also abiotic stress responses (Cao et al., 2015; Ma et al., 2011; Ruan et al., 2011). In the present study, an increase in the gene expression levels of Glycine max putative phytocyanin has been observed in roots of Augusta and Fiskeby V – the two cold‐resistant varieties. On the contrary, in cotyledons, the expression levels were decreased in Augusta, Fiskeby V and Toyomusume. The differential expression profiles of enzymes involved redox homeostasis and electron transport, which may be responsible to some extent for increased/decreased susceptibility to abiotic stresses. However, more in‐depth analyses are necessary to decipher the mechanism underlying the observed changes.Metabolic pathways affected in soybean under chillingIn the present study, we performed the GO analysis and KEGG pathway classification of genes predicted to be targeted by miRNAs that were differentially expressed under chilling stress. According to these analyses, terms that were highly represented included cellular nitrogen compound metabolism, cellular protein modification and stress response. These processes were strongly related to plant abiotic stress resistance. For instance, various protein modifications such as ubiquitination, sumoylation and phosphorylation activate numerous transcription factors crucial in abiotic stress response (Kosová et al., 2018). Furthermore, auxin‐activated signalling, regulation of transcription and secondary metabolism also constituted a substantial part of these classifications, suggesting a profound contribution in survival under low‐temperature conditions. It has been established that auxins assist plants in coping with environmental stresses by regulating transcription factors and modulating growth and development (Bielach et al., 2017). Furthermore, secondary metabolites such as flavonoids, isoprenes and cinnamic acid derivatives that are known to be overproduced due to chilling stress can neutralize ROS (Isah, 2019; Yang et al., 2018).It has been shown that reprogramming of the central carbohydrate metabolism plays a key role in cold acclimation in plants (Fürtauer et al., 2019; Hoermiller et al., 2017; Ritonga & Chen, 2020). These findings were corroborated in our study, as seen in the results of KEGG analysis, where it has been shown that sugar metabolism was among the pathways affected mostly by the 16 differentially expressed miRNAs. In Arabidopsis, starch metabolism is considered as a determinant of plant fitness under abiotic stress as it responds with great plasticity to various growth conditions. Additionally, various sugars stabilize biological membranes, liposomes, act as osmoprotectants or even stabilize photosynthesis during stress as the reduction in photosynthetic capacity is often accompanied by increased sugar accumulation (Fürtauer et al., 2019; Hajihashemi et al., 2018). Photosynthesis and CO2 fixation were also affected and negatively regulated by cold stress (Banerjee & Roychoudhury, 2019; Calzadilla et al., 2019; Hajihashemi et al., 2018), which was further confirmed in our studies, as enzymes involved in carbon fixation, photosynthesis and nicotinate and nicotinamide metabolism were among the targets of miRNAs with changed expression levels under cold stress. Glutathione, organic sulphur repository, in its reduced form (GSH) is an essential metabolite in various biosynthetic pathways like detoxification and redox homeostasis (Rao & Reddy, 2008). To date several reports indicated the involvement of glutathione in responses to abiotic stresses (Hasanuzzaman et al., 2017; Kocsy, Szalai, et al., 2000, Kocsy, Von Ballmoos, et al., 2000; Spanò et al., 2017). At low non‐freezing temperatures, high GSH content and glutathione reductase activity were detected in several plant species, indicating a possible contribution to chilling tolerance and cold acclimation (Kader et al., 2011). Glutathione metabolism was one of the pathways indicated in our KEGG analysis, where three enzymes: glutathione reductase (ec:1.8.4.2), 5‐oxoprolinase (ATP‐hydrolysing) (ec:3.5.2.9) and dehydrogenase (NADP+) (ec:1.1.1.49) were predicted as targets of miRNAs with changed expression under chilling. The classification of these metabolic changes may shed light on the role of target genes of differentially expressed miRNAs in stress responses of soybean.CONCLUSIONSCold stress is one of the major environmental factors that severely affects plant growth and development and negatively influences crop productivity. Some plants are able to cope with this stress and acquire chilling tolerance; in some species/varieties/single individuals, the exposure to this stress triggers developmental responses. As tender legumes, soybeans thrive in warm climates and are sensitive to cold. In the present study, to determine the involvement of miRNAs and their target genes in chilling resistance of four soybean cultivars varying in cold stress susceptibility, high‐throughput sequencing was used to identify cold‐responsive miRNAs and their target genes. A total of 321 known miRNAs were identified, and 348 novel miRNAs were predicted, of which 162 miRNAs, including well‐conserved, legume‐ and soybean‐specific miRNAs, and 18 novel miRNAs, respectively, had changed expression profiles. Interestingly, several miRNAs such as miR156, miR169 and miR5770 had similar expression patterns in Augusta, Fiskeby V and G. soja, which clearly contrasted from that in cold‐sensitive Toyomusume variety. Altogether, the results suggest that these miRNAs may play a role in the chilling responses of soybean. Degradome analysis as well as GO and KEGG annotations allowed us to assign potential target genes to the differentially expressed miRNAs. Many of these genes were found to be related to plant abiotic stress response mechanisms such as ROS scavenging, flavonoid biosynthesis and regulation of osmotic potential. In summary, our findings provide valuable insights into the function of miRNAs in the soybean chilling resistance and may provide crucial knowledge in the development of new cultivars. Investigating the molecular mechanisms of soybean chilling stress responses will facilitate better understanding of the response of plant species to chilling and help to reduce the consequences of this major environmental stress on plants.Sequence dataThese sequence data have been submitted to the Sequence Read Archive (SRA) BioProject (NCBI) databases under accession number PRJNA725380.ACKNOWLEDGEMENTSThe work was supported by a grant no. UMO‐2014/15/B/NZ9/02312 from the National Science Centre, Poland, and the Ministry of Science and Higher Education of the Republic of Poland via the KNOW program. We are thankful to Prof. J. Nawracała for providing the seeds of the soybean cultivars for the experiments.CONFLICT OF INTERESTThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.AUTHOR CONTRIBUTIONSA.T., J.G.‐B. and T.T. – conceptualization; A.T., J.G.‐B. and W.K. – methodology; A.T., J.G.‐B. and W.K. – validation; T.T. – formal analysis; J.K., A.T., J.G.‐B. and W.K. – investigation; T.T. – resources; A.T. and J.G.‐B. – data curation; J.K. – writing–original draft preparation; J.K., A.T., J.G.‐B., W.M.K. and T.T. – writing–editing; J.K. – visualization; A.T. – supervision; A.T. and J.G.‐B. – project administration; T.T. – funding acquisition. All authors have read and agreed to the published version of the manuscript.DATA AVAILABILITY STATEMENTData openly available in a public repository that does not issue DOIs. 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Trends in Plant Science, 25(5), 446–454. https://doi.org/10.1016/j.tplants.2020.01.010 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Agronomy and Crop Science Wiley

Cold stress‐induced miRNA and degradome changes in four soybean varieties differing in chilling resistance

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Abstract

INTRODUCTIONSoybean [Glycine max (L.) Merr.] is one of the most important oilseed plants cultivated worldwide, with a production of 347 million metric tonnes in 2017 (https://www.soymeal.org/soy‐meal‐articles/world‐soybean‐production/). It is an exceptional source of oils and proteins used in human nutrition and in animal feed formulation. Additionally, soybean‐derived oils are used in the production of biofuels (Wang & Komatsu, 2018). Symbiosis between soybean and Bradyrhizobium japonicum results in the formation of root nodules (Egamberdieva et al., 2018). The beneficial role of binding of atmospheric nitrogen is a distinctive aspect of soybean cultivation that considerably improves soil composition and helps to reduce the use of fertilizers.An important issue of soybean cultivation in high latitudes is the lack of tolerance to low temperatures, which affects the nodulation process as well as the general growth and yield quality of the plants (Zhang et al., 2014). It has been predicted that in the future, due to climate change, the frosts will become more severe, especially upon seed set (Redden, 2021). Soybean requires relatively high temperatures for germination, growth, development and maturation, and the suitable temperature for soybean is 15–22°C at emergence, 20–25°C at flowering and 15–22°C at maturity (Liu et al., 2018). In temperate climatic conditions, soybeans may be exposed to chilling stress mainly during two periods. The first period is the emergence and early vegetative stages of plant development (V1–V3; Nleya et al., 2019), that is, from the last 10 days of April to mid of May. It has been suggested that the first hours of imbibition are crucial and that low‐temperature–caused injuries are expressed as both reduced emergence of seedlings and reduced vigour and yield of surviving plants (Bedi & Basra, 1993; Skrudlik & Kościelniak, 1996). This issue is of major agricultural importance because germination in cold soil can markedly reduce productivity. The second most sensitive to chilling period is during the flowering phase because pod formation is a process critical for legume crop productivity. Temperatures of 15°C in day and between 15 and 9°C in night are biological minimums at this growth stage (Hume & Jackson, 1981a). The sensitivity of soybean to night temperatures below 15°C is reflected in changes occurring in metabolism, growth, development and yield (Alsajri et al., 2019; van Heerden et al., 2003; Kurosaki et al., 2003; Tyczewska et al., 2016). Exposure to low temperatures causes the accumulation of osmoprotectants such as proline and sucrose as well as reorganization of the membrane structure (Ahmad & Majeti, 2012; Michaelson et al., 2016). Commonly, under chilling conditions, the expression levels of stress‐responsive genes, including many transcription factors and enzymes, are affected (Baillo et al., 2019). Moreover, a single night of cold, with minimum temperatures of 8°C, is sufficient to inhibit pod formation (Hume & Jackson, 1981b). Thus, under natural growth conditions, soybean yield is resilient to cold temperatures that fall to as low as 15°C. However, temperatures below this level pose a significant risk for reducing yield, especially when they fall to 10°C.One of the mechanisms that regulate gene expression in plants relies on microRNAs (miRNAs). These are small noncoding RNAs that interact with genes involved in the regulation of plant growth, development and response to abiotic and biotic stresses (Hume & Jackson, 1981a). These interactions comprise mRNA cleavage based on the binding to complementary sequences within its target genes as well as translation inhibition by restriction of the ribosome binding process (Bartel, 2004). To date, a number of miRNAs in soybean has been reported to be associated with response to abiotic and biotic stresses such as drought, salinity (Liu et al., 2016), phosphate starvation (Xu et al., 2013), cold (Kuczyński et al., 2020) and cyst nematode infection (Li et al., 2012). However, the involvement of miRNAs in the chilling stress response in soybean is poorly explained and needs further investigation.In this study, we compared the responses of four soybean cultivars differing in cold stress tolerance (Augusta, Fiskeby V, Toyomusume and Glycine soja) to chilling stress at the molecular level, with a focus on miRNAs and their target genes. The assessment of cold stress effects in three tissues [shoots (trifoliates), seedling roots and cotyledons] facilitated the comprehensive investigation of stress response in soybean early growth stages. The sequencing of small RNAs isolated from plants cultivated under control and stress conditions allowed to characterize miRNAs involved in the chilling stress responses. Additionally, the sequencing of degradome enabled to identify potential target genes of differentially expressed miRNAs in soybean. This study aimed to determine the responses of the above‐mentioned four soybean cultivars to chilling stress at the molecular level and to elucidate the role of specific miRNAs in soybean tolerance system. Our results provide insights into cold stress response mechanisms and the regulatory roles of miRNAs in cultivated and wild soybean cultivars.MATERIALS AND METHODSPlant materialFour soybean varieties were chosen for the experiment: Augusta, Fiskeby V, Toyomusume and G. soja. Fiskeby V was bred by Dr. Sven A. Holmberg in Sweden, near the city of Norrkoping (58°30′N). Augusta was selected from two crosses: in the first step, a cross was made between Fiskeby V and line PI 194643, and line 104 was obtained; in the second step, line 104 was crossed with line 11 (G. soja wild species). Line 11 of G. soja grows in the natural environment of far Eastern Russia at latitudes similar to those of Poland and has a long‐day–tolerant genotype. Therefore, Augusta has two sources of photoperiod insensitivity, and its chilling tolerance is derived from Fiskeby V. The seeds of the Augusta and Fiskeby V soybean cultivars were provided by Prof. J. Nawracała from the Poznan University of Life Sciences, Poland. The chilling tolerance of the Swedish cultivar Fiskeby V is presumed to be derived from the Sakhalin landrace Namikawa. Glycine soja is a wild soybean annual species that is native to China, Japan, Russia and parts of Korea and is a wild progenitor of the cultivated species G. max. Glycine soja accession PI 538411A was collected over Amur River (Far East of Russia) on latitude: 52°58′39″N and longitude: 127°21′44″E. Toyomusume was chosen as a chilling‐sensitive genotype. It is a Japanese variety from Hokkaido Island, where it is cultivated mainly for tofu production.Prior to sowing, the soybean seeds were inoculated with B. japonicum (HiStick® Soy, BASF) to induce nodule formation. The soybean varieties Augusta, Fiskeby V, Toyomusume and G. soja were planted in pots filled with a mixture of all‐purpose potting soil and sand in the ratio of 3:1. Plants were grown under controlled environmental conditions in a phytotron at 20°C with a relative humidity of 60% and a 16:8‐h light: dark photoperiod prior to stressing treatment.The plants were divided into two groups, with each group subjected to chilling treatment at a different developmental stage (Table 1). The first batch of plants was stressed at the VE stage (emerging seedlings) by keeping them at 4°C for 48 h in Percival chambers. The next batch of plants was exposed to 8°C for 120 h (5 days) at the V1 growth stage (first trifoliate). In the control and treated groups, 20 to 30 soybean plants were cultivated.1TABLEScheme of the chilling stress treatmentStage of soybean growthOptimal growth temperature (°C)Stress temperature (°C)Duration of stress conditions (h)VE – seedlings20448V1 – vegetative208120sRNA library construction and sequencingTotal RNA was isolated from the trifoliates, seedling roots and shoots of the four soybean genotypes by using Direct‐zol™ RNA Miniprep Plus (Zymo Research) according to the manufacturer's instructions. The quantity and purity of the total RNA were checked using NanoDrop ND‐1000 (NanoDrop) and Agilent 2100 with a minimum RNA integrity number (RIN) threshold value of >8.0. Approximately 5–10 µg of total RNA was used for the sRNA library construction according to the protocol of TruSeq Small RNA Sample Prep Kits (Illumina). A total of 72 soybean sRNA libraries were constructed, from both chilled and control seedling roots and cotyledons as well as V1 stage shoots, all performed in triplicate. The libraries were sequenced on an Illumina Hiseq4000 at BGI Tech Solutions Co., Ltd.Bioinformatics analysis of sequencing dataThe sequencing data were filtered to contain only fragments of at least 18 nt in size. The sequences were then further processed to contain at least five counts for each variety/tissue combination. The raw counts were subsequently analysed using edgeR Bioconductor package in R following the developer's instructions (Robinson et al., 2009).The known miRNA sequences were identified using BLAST against all miRBase mature sequences downloaded in March 2019. Only sequences identical to the reference miRNAs were retained for further analyses. Novel miRNAs were searched using the miRDeepP2 program (Kuang et al., 2018) with default options and using G. max reference genome version 2.1 downloaded from ENSEMBL in May 2019. Only sequences that did not map to the reference miRBase dataset were retained for further analyses.ddPCR analysisThe profiles of four differentially expressed miRNAs were assayed by ddPCR. A set amount of extracted RNA (1 µg sRNA and 1.5 µg of total RNA) was reverse transcribed using SuperScript IV Reverse Transcriptase (Thermo Fisher Scientific) as described in Varkonyi‐Gasic et al. (2007). Stem‐loop primers were designed for the miRNA reverse transcription reactions. The RT‐PCR primers are listed in Table S1. To quantify the number of miRNA molecules in the plant samples, a ddPCR mixture containing 10 µl of ddPCR SuperMix Eva Green (BioRad), primers (the final concentration of each primer was 200 nM), template (reverse‐transcribed, elongated miRNA) and RNase‐free H2O was used. A 20 µl reaction mixture was used to generate the droplets in an eight‐well cartridge by using a QX100 droplet generator (Bio‐Rad). The droplets were carefully transferred to a 96‐well ddPCR plate and heat‐sealed with a foil (Bio‐Rad). The cDNA was then amplified in a T100 PCR thermal cycler (Bio‐Rad) under the following cycling conditions: 5 min of denaturation at 95°C, followed by 40 cycles with a three‐step thermal profile of 30 s of denaturation at 95°C, 30 s of annealing at 55°C and 45 s of extension at 72°C. Subsequently, the products were maintained at 72°C for 2 min for final extension. After amplification, the products were cooled to 4°C for 5 min, heated to 90°C for 5 min and finally cooled again to 12°C. The droplets were quantified in a QX100 droplet reader (Bio‐Rad). Data acquisition and analysis were performed using QuantaSoft software (Bio‐Rad). The positive droplets containing the amplification products were differentiated from the negative droplets by setting the fluorescence amplitude threshold to the lowest value of the positive droplet cluster. Yeast tRNA‐Thr(TGT) molecules with a scrambled sequence were added to each RT reaction mixture as an internal control.Degradome sequencingThe quantity and purity of the total RNA were checked using NanoDrop ND‐1000 (NanoDrop) and Agilent 2100 with a minimum RIN threshold value of >7.0. Approximately 20 µg of total RNA was used to prepare 24 degradome libraries, with three biological replicates for both stress and control conditions of the four cultivars. The mRNA was mixed with biotinylated random primers, and the RNA containing the biotinylated random primers was then captured by beads and ligated to adaptors, according to the BGI Tech Solutions Co., Ltd., procedure. Following purification, digestion, ligation and repurification, the cDNA library was sequenced (single end, 36 bp) with an Illumina Hiseq4000 at BGI Tech Solutions Co., Ltd. Clean reads of the degradome sequencing were obtained by removing low‐quality reads and the adapter contaminants. The clean reads of the degradome data were then mapped to the soybean (G. max) genome. CleaveLand pipeline 4.3 (https://sites.psu.edu/axtell/software/cleaveland4/) was used to confirm the potential miRNA: target gene pairs with default parameters.The putative targets for the selected sRNA reads were identified using CleaveLand4‐4.5 (Addo‐Quaye et al., 2009) program according to the developer's instructions. Prior to the analysis, the degradome data were adapter and quality trimmed using the cutadapt tool (Martin, 2011). mRNA sequences downloaded from the NCBI RefSeq database in June 2019 were used as a reference for miRNA target detection.Gene expression analysis using real‐time PCRTotal RNA was isolated from frozen powdered samples (100 mg) using NucleoSpin RNA Plant (Macherey‐Nagel) following the manufacturer's protocol. Total RNA (2 µg) was used for cDNA synthesis with the Transcriptor First‐Strand cDNA Synthesis Kit (Roche) using oligo (dT) primers according to the manufacturer's instructions. For real‐time PCR analysis, the cDNA template was diluted two times. Real‐time PCR analysis was performed to determine the expression levels of the Glycine max putative phytocyanin (Phyt), Glycine max transcriptional factor NAC‐19 (NAC‐19) and Glycine max malate dehydrogenase [NADP] (GmMDH), and each cDNA sample was analysed using Mono Color Hydrolysis UPL Probes (Roche) selected for each gene by using ProbeFinder Software (Roche). The PCR reaction mixtures were prepared according to the manufacturer's protocol. PCR conditions were as follows: initial incubation step at 94°C for 10 min, followed by 45 cycles of amplification (15 s at 94°C, 30 s at 60°C and 15 s at 72°C; single acquisition), with a final cooling step at 40°C for 2 min. The analysis was performed using a LightCycler 480 II instrument (Roche). Relative gene expression was calculated using the Roche Applied Science E‐Method and normalized to the three reference genes (Glycine max actin‐6 (ACT‐6), SKP1/Ask‐Interacting Protein 16 (SKIP16) and Eukaryotic elongation factor 1 β (ELF1B; Hu et al., 2009). All standard curves were generated by amplifying a series of twofold dilutions of cDNA. The primer sequences for the analysed genes and UPL probes are given in Table 2.2TABLEList of primers and UPL probes used for real‐time PCR analysisGene shortcutFull gene nameForward primer (5′−3′)Reverse primer (5′−3′)UPL no.ACT‐6 aGlycine max actin‐6CAGGAATGGTTAAGGCTGGTCGAGGACGACCTACAATGCT67SKIP16 aSKP1/Ask‐Interacting Protein 16GGGATGGGATGGGATAGAATTCCCAAATAATGAAATTGAGACTTC31ELF1B aEukaryotic elongation factor 1 βAGTTTTGTTTTTCTTTATTTGAATTGCCAGCGCCACTGAATCTTACC72PhytGlycine max putative phytocyaninGCACCACAGGCAATTCACTAGGCAGTCTTGAGGGTGATTG59NAC20Glycine max transcriptional factor NAC‐19CGAAATGGAAGACGTGAAGCTCGGAAGCCTCGAAGTACAG70GmMDHGlycine max malate dehydrogenase [NADP]AAGATGCAGAATGGGCTTTGGCCCATTTATGTCTAACAAGTCTG85aReference genes are marked in bold.GO analysisGO functional classification was performed using Blast2GO (https://www.blast2go.com/) with the default settings. The sequences were blasted using NCBI BLAST service (QBlast) and a blastx‐fast program. Blast expectation value (E‐value) was set at 1.0E−3. Next, the GO ontology mapping and InterProScan were performed. GO mapping was performed against extensively curated gene ontology annotated proteins to obtain functional labels. The used data originate from the Gene Ontology Association and Uniprot ID‐Mapping. The public EMBL‐EBI InterPro web service was used to scan sequences against InterPro's signatures with the default settings. Go annotation was performed with the annotation cut‐off set at 55 and E‐value hit filter set at 1.0E−6. Enrichment analysis was performed using Fisher's exact test.RESULTSAnalysis of small RNA sequencing librariesTo identify the miRNAs involved in the chilling stress response, 72 sRNA libraries were constructed for cold‐stressed samples, and their corresponding controls that were sequenced by Illumina Technology. The number of raw sequencing reads per sample ranged from 20.2 to 43 million (Table 3).3TABLEStatistics of soybean sRNA sequencing librariesSampleTotal raw readsUnique raw readsTotal filtered readsUnique filtered readsARC31748684467279825733227348281ARS27488230404280322482467340208,7FRC35274590575501927375919421643,3FRS33272581461187227384545402207,3GRC31371582452014425182469311193GRS32342809393441727081801294042,7TRC34799529507382727851481408338,3TRS32134038435888626684040394515,3ACC34831334388114428492562458136,7ACS32385665364430226485865438347,7FCC25825875220820922059012289776,7FCS26971906218295523513888277404,7GCC32915940440745426261721392516GCS31400723356112226232399362572,3TCC30884146279010225515349348449,3TCS34427857317006229265725369728,3ASC33943160381612127873908456142,3ASS31835213374930426172147453468,7FSC31749968387572225430403424815,3FSS32928969286875328472060382114GSC36552754468699729270721423320GSS30993728417783924756494413838,3TSC31087038344866925297087420317TSS36333521326983629412734406057,7aFirst letter of the sample name designates cultivar: A – Augusta, F – Fiskeby V, G – Glycine soja, T – Toyomusume; second letter designates tissue: R – root, C – cotyledon, S – shoot; third letter designates conditions: C – control, S – stress.After removal of the adapter sequences and low‐quality reads, the total read count ranging from 16.6 to 35.9 million and the unique read count ranging from 240 to 525 thousands were obtained from these 72 libraries (Table 3). Quantification of miRNAs between the cultivars and treatments was preceded by the normalization of expression levels of the miRNA families to counts per million (cpm). The normalized miRNA families read frequencies ranged from 1 to 552,267 cpm for the trifoliates of V1 stage, from 1 to 134,679 cpm for the root samples and from 1 to 165,310 cpm for the cotyledon samples (Table 3). Size distributions based on the filtered reads were assessed and are shown in Figure 1. The majority of mapped sequences were found to be 21 nt long (18.1%), followed by sequences that were 20 and 22 nt long (12.7% and 12.3% respectively).1FIGURESequence length distribution of miRNAs in sRNA libraries of soybeanIdentification of known and novel miRNAs in soybeanThe filtered reads from 72 sRNA libraries, from both chilled and control seedling roots and cotyledons as well as V1 stage shoots, all performed in triplicate, were compared with the collection of mature (and precursor) soybean miRNAs from miRBase release 22.1. A total of 321 known soybean miRNA families were identified in all four cultivars (Table S2). In cotyledons of soybean at the VE stage, the most abundant miRNA family was miR159, except for Fiskeby V in which miR165 was the most highly expressed miRNA. In roots (radicles) of all plants collected at the seedling (VE) stage, the most extensively expressed miRNA family was miR319. In trifoliates of soybean collected at the V1 stage, the most abundant miRNA family was miR398. Additionally, among the highly expressed miRNA families in all the studied samples, there were mostly conserved miRNAs such as miR159, miR165, miR166, miR167, miR319, miR396, miR398, miR408 and miR482 and legume‐specific miRNAs such as miR4414, miR1510 and miR3522. On the other hand, among the least expressed miRNA families, the ratio of conserved miRNAs to legume‐specific miRNAs was substantially lower than that in highly expressed fraction. Most of the conserved miRNAs among the least abundant miRNA families were transcriptional variants of their highly expressed counterparts.Additionally, 348 novel miRNAs were found in the four studied cultivars (213 miRNAs in Augusta, 220 in Fiskeby V, 224 in Toyomusume and 218 in G. soja) (Table S3), among which 129 miRNAs were common for all four cultivars.Differential expression of miRNAs in soybeanTo identify the miRNAs that exhibit differential expression patterns under chilling stress, we used fold‐change values of 1 and −1 and p‐value of .05 as the threshold. A total of 162 differentially expressed miRNAs belonging to 137 miRNA families were identified between chilled and control samples of the four soybean cultivars. The distribution of differentially expressed miRNAs in shoots, cotyledons and roots of all studied varieties is given in Figure 2.2FIGUREQuantitative distribution of differentially expressed miRNAs in shoots, cotyledons and roots of the studied cultivars; S – shoots, R – roots, C – cotyledonsAmong these differentially expressed miRNAs in all the tested cultivars, 93 were downregulated in chilled samples as compared to those in controls in at least one cultivar, 28 of which were downregulated by at least fivefold. MA plots of differentially expressed miRNAs in four studied cultivars are given in Figure 3.3FIGUREMA plots of differentially expressed miRNAs in four studied cultivars; red dots – upregulated, blue dots – downregulatedOn the other hand, 137 miRNAs were upregulated in samples from stressed plants compared to those in controls in at least one cultivar, 41 of which were upregulated by at least fivefold (Figure 3). The most prominent reduction in expression during chilling stress was noted for miR169, miR166, miR159 and miR5037 in cotyledons (VE stage); for miR159, miR160, and miR171 in seedling roots (VE stage) and for miR167, miR2111, miR5371, miR5037, miR398, miR4416 and miR160 in trifoliates from V1 stage. Conversely, the most remarkable increase in the expression levels was observed for miR6300, miR5368, miR6173 and miR1509 in cotyledons (VE stage); for miR319, miR9750, miR408 and miR2109 in seedling roots (VE stage) and for miR166, miR398, miR2119, miR399, miR4996, miR171, miR6300, miR5368, and miR169 in the trifoliates from V1 stage among the four cultivars. Several miRNA families, including miR159, miR319 and miR482, were differentially expressed universally in seedling roots, with miR159 and miR482 being downregulated in all the four cultivars, whereas miR319 was downregulated in Augusta and G. soja and upregulated in Fiskeby V and Toyomusume. Similarly, in the trifoliates of plants stressed at V1 stage, there were miRNAs differentially expressed in all the studied soybean cultivars, including miR10197, miR1507, miR1509, miR159, miR166, miR2111, miR3522, miR396, miR398, miR408 and miR4996, where miR10197 and miR2111 were downregulated, miR1509 and miR396 were upregulated in all cultivars, while miR1507, miR159, miR166, miR3522, miR398, miR408 and miR4996 were downregulated in Augusta and G. soja and upregulated in Fiskeby V and Toyomusume (Figure 4).4FIGUREHeatmap of differentially expressed miRNAs in trifoliates of four studied soybean cultivarsRegarding the differences in expressional patterns between the four cultivars, several notable miRNAs showed contrasting expression in the chilling‐sensitive cultivar Toyomusume and the chilling‐tolerant cultivars Augusta, Fiskeby V and G. soja. In the seedling roots of Augusta, Fiskeby V and G. soja, miR169 and miR5770 were found to be downregulated by 2.3‐, 3.3‐ and 2.4‐fold and by 2.9‐, 1.7‐ and 2.1‐fold, respectively, while their expression was not significantly altered in Toyomusume, thus suggesting their involvement in the chilling stress response (Figure 5). Similar patterns were found in trifoliates (V1 stage) for miR156 and miR5770, where they were downregulated by 1.7‐, 3.9‐ and 1.9‐fold and by 2.1‐, 3.6‐ and 2.3‐fold in Augusta, Fiskeby V and G. soja, respectively, while their expression was again not altered significantly in Toyomusume. Interestingly, there were seven miRNAs (miR1507, miR159, miR166, miR 3522, miR398, miR408 and miR4996) with common expression trend of upregulation in Toyomusume and Fiskeby V, while being downregulated in Augusta and G. soja.5FIGUREHeatmap of differentially expressed miRNAs in roots of four studied soybean cultivarsAdditionally, 18 novel miRNAs were differentially expressed between stressed and control samples. Among them, four miRNAs were found in more than one sample. Two of them were common for cotyledons and trifoliates of G. soja, one was shared by trifoliates of G. soja and Augusta and one by trifoliates of G. soja and Fiskeby V.Validation of miRNA expression by ddPCRTo verify the reliability of sequencing results, the expression levels of four miRNAs (miR169, miR408, miR2109 and miR5770) responsive to chilling stress were evaluated by ddPCR (Figure 6). The ddPCR results showed that the expression patterns of four selected miRNAs were mostly in accordance with the assessment of sRNA sequencing (except for miR169 in Augusta and Fiskeby V, and miR408 in Toyomusume). The discrepancies in the fold change of particular samples can be attributed to the differences in the sensitivity and specificity of the two techniques. Moreover, in ddPCR, it is difficult to differentiate between the particular miRNAs belonging to one miRNA family, which may further explain some differences observed between sequencing and ddPCR results.6FIGUREValidation of expression of four miRNAs at two developmental stages of four soybean cultivars. AS – Augusta roots, FR – Fiskeby V roots, TR – Toyomusume roots, GR – G. soja roots, AS – Augusta seedlings, FS – Fiskeby V seedlings, TS – Toyomusume seedlings, GS – G. soja seedlingsmiRNA target profiling by degradome sequencingOn the basis of the degradome sequencing and subsequent CleaveLand program analysis, we identified potential targets of soybean miRNAs that are involved in the chilling stress responses. In total, 2005 targets were identified in all 24 libraries, among which 1183 belonged to Category 0, 222 to Category 1, 293 to Category 2, 164 to Category 3 and 143 to Category 4, where Category 0 represented the best match between miRNA and target genes (Table S4). Further investigation revealed potential targets of differentially expressed miRNAs (Table 4). Examples of miRNAs alignments and their T‐plots validated by degradome sequencing are given in Figure S1.4TABLETarget genes of some differentially expressed miRNAs involved in soybean chilling stress responsemiRNA IDTranscriptTranscript annotationCleavage siteDegradome categoryDegradome p‐valuegma‐MIR1507a+|gso‐MIR1507a+|vun‐MIR1507a+|vun‐MIR1507b+XM_003538342.4PREDICTED: Glycine max aspartic proteinase PCS1 (LOC100803089)2772.038714958gma‐MIR166r+NM_001250783.3Glycine max GAMYB‐binding protein (LOC732608)17433.091742104crt‐MIR166b+|csi‐MIR166f+|gma‐MIR166r+XM_003524945.4PREDICTED: Glycine max homeobox‐leucine zipper protein ATHB‐14‐like (LOC100806322)10550.0012395gma‐MIR3522+|gso‐MIR3522a+|gso‐MIR3522b+NM_001248914.2Glycine max zinc finger protein CONSTANS‐LIKE 2 (COL2B)10170.03432688gso‐MIR3522a+|gso‐MIR3522b+NM_001248512.2Glycine max Cu/Zn superoxide dismutase copper chaperone (CCS)7410.02167003gma‐MIR397a+NM_001354136.1Glycine max NAC transcription factor (LOC100814504)6173.026758707fve‐MIR397+|gma‐MIR397b+|lja‐MIR397+XM_003529084.3PREDICTED: Glycine max laccase‐7 (LOC100814926)7410.002099926MIR408‐|ath‐MIR408+|gma‐MIR408a+|gma‐MIR408b+|gma‐MIR408c+|gma‐ MIR408+|zma‐NM_001255827.1Glycine max NAC domain protein (NAC19)7260.004468gma‐MIR5770a+NM_001358538.1Glycine max putative copper amine oxidase (LOC100819571)2282.019549gma‐MIR482b+|gma‐MIR482d+|gma‐MIR482e+|gso‐MIR482a+|gso‐MIR482b+NM_001254425.2Glycine max TIM21‐like protein, mitochondrial‐like (LOC100779212)2440.028829MIR398b+|gma‐MIR398c+|gma‐NM_001248369.1Glycine max superoxide dismutase [Cu‐Zn] (SOD1)220.002981MIR398b+|gma‐MIR398c+|gma‐NM_001248512.2Glycine max Cu/Zn superoxide dismutase copper chaperone (CCS)7403.069627In seedling roots, miR159 targeted cytochrome P450 and pyruvate dehydrogenase (E1 component subunit alpha‐3), while miR319 targeted TCP transcription, a protein from flavin‐binding monooxygenase family, and cold‐regulated protein (COR1; in G. soja). One of the most represented targets of miR482 was TIM21‐like protein. Regarding the miRNAs and their targets in trifoliates of V1 stage, miR1507 was assigned to aspartic proteinase PCS1 and NBS‐LRR disease resistance protein; miR156 was assigned to scarecrow‐like protein 28, squamosa promoter binding‐like protein (in Augusta) and FAD synthase (in G. soja); miR159 targeted CDPK‐related kinase and pyruvate dehydrogenase (E1 component subunit alpha‐3); miR166 targeted GAMYB transcription factor and homeobox‐leucine zipper protein ATHB‐14 like; miR2111 was found to cleave VSPA (vegetative storage protein A); miR3522 targeted COL2a (CONSTANS‐like 2a); miR398 affected the expression of SOD (superoxide dismutase [Cu‐Zn]) and CCS (Cu/Zn superoxide dismutase copper chaperone) (except for Toyomusume); miR408 was considered to alter the expression of HMA8 (chloroplast copper‐translocating HMA8 P‐ATPase) and NAC domain protein; and miR4996 targeted cysteine proteinase, polyphenol oxidase and omega‐6 fatty acid desaturase (FAD).Gene ontology terms were assigned to 378 target genes controlled by 16 differentially expressed miRNAs (Table S5). Target genes were described by 53 terms in biological process category, 31 terms in molecular function category and 25 terms in cellular compartment category. Highly represented terms included ‘biosynthetic process’, ‘cellular nitrogen compound metabolic process’ and ‘cellular protein modification process’ in biological process category; ‘ion binding’, ‘DNA binding’ and ‘oxidoreductase activity’ in molecular function category; and ‘cell nucleus’, ‘protein containing complex’ and ‘membrane’ in cellular compartment category (Figure 7). Moreover, according to the KEGG analysis, 378 target genes were significantly enriched in 67 pathways, of which the largest number of enzymes affected by the cold stress were those related to sugar metabolism: starch and sucrose metabolism (6), glycolysis/gluconeogenesis (5), galactose (5), amino sugar and nucleo sugar (5) and pyruvate (8) metabolism, pentose phosphate pathway (3) and pentose and glucuronate interconversion (3). Additionally, enzymes involved in carbon fixation in photosynthetic organisms (5), photosynthesis (1) and nicotinate and nicotinamide metabolism (1) were affected after cold treatment in soybean. Glutathione metabolism (3) was also altered due to chilling. The metabolism of various amino acids (including tyrosine, phenylalanine, glycine, serine, threonine, arginine and proline), purine and biotin, isoquinoline alkaloid biosynthesis as well as pyruvate and thiamine metabolism were also indicated as pathways influenced by cold stress (Table S6).7FIGUREGene ontology (GO) analysis of target genes of differentially expressed miRNAsGene expression level analysis using real‐time PCRThree genes were selected for the analysis of changes in gene expression level under chilling in roots and cotyledons of four tested soybean varieties: Glycine max putative phytocyanin (Phyt, NM_001251440.2), Glycine max transcriptional factor (NAC‐19, NM_001255827.1) and Glycine max malate dehydrogenase [NADP] (GmMDH, NM_001369219.1). In roots, following stress exposure, Phyt was upregulated in Augusta and Fiskeby V, contrary to Toyomusume and G. soja, where a significant downregulation of the gene's expression level was observed. NAC‐19 transcription factor was highly upregulated in roots in all tested varieties. GmMDH in roots was upregulated in Augusta, Fiskeby V and Toyomusume, 50% downregulation was noted for G. soja. In cotyledons, the expression of Phyt was downregulated (by at least 50%) in all varieties except for G. soja, where it was upregulated (by 40%). NAC‐19 transcription factor was upregulated in cotyledons of all tested varieties, and GmMDH was upregulated in Augusta and Toyomusume and downregulated in Fiskeby V and G. soja. The graphs presenting changes in the expression levels of chosen genes are presented in Figure 8.8FIGUREThe relative gene expression levels (Y axis) of selected genes (Phyt, NAC‐19 and GmMDH) in soybean varieties under chilling. (a) In roots, (b) in cotyledons. The results are presented as the mean ± SD, from two independent experimental and three technical repeats (treated vs. control). First letter of the sample name designates cultivar: A – Augusta, F – Fiskeby V, G – Glycine soja, T – Toyomusume; second letter designates tissue: R – root, C – cotyledon; third letter designates conditions: C – control, S – stressDISCUSSIONLow‐temperature conditions are one of the critical factors that influence plant growth, development and geographical distribution (Megha et al., 2018). Cold injury can affect plants in several ways, such as by causing disruption of energy generation by blocking photosynthesis, generating systemic oxidative stress caused by excessive production of reactive oxygen species (ROS) and disrupting membrane transport associated with reduced fluidity of the plasma membrane structure (Miura & Furumoto, 2013). miRNAs are crucial players in the regulation of various stress responses and constitute a major part of sequence‐specific gene silencing machinery (Kumar, 2014). Nevertheless, the involvement of miRNAs in the chilling stress response in soybean needs further investigation. The present study aimed to identify miRNAs that participate in soybean's response to chilling stress. To achieve our objective, we used four cultivars (Augusta, Fiskeby V, Toyomusume and G. soja) that differ in their sensitivity to chilling stress. In addition to the traditional comparison of treated and control groups, Toyomusume, as the cultivar susceptible to low temperatures, served as the background in the analysis of the differential expression patterns of stress‐responsive miRNAs. On the basis of the comparison of the aforementioned cultivars and their tissues sampled at two soybean developmental stages, we concluded that miRNAs play an important role in soybean's chilling stress tolerance mechanism. Additionally, we identified 321 known miRNAs along with 348 novel miRNAs in 72 libraries from the four tested soybean cultivars.miRNA in chilling stress responses in soybeanOur approach based on the use of high‐throughput methods broadens the knowledge of mode of action of miRNAs in soybean plants. A previous study showed that cold stress influences the growth of two tested varieties, namely Augusta and Fiskeby V, which was observed as phenotypic changes (Kuczyński et al., 2020). It was also shown that under chilling stress, the expression levels of several miRNAs (miR169, miR319, miR397 and miR398) and their target genes had changed. In the present study, amid the plethora of miRNAs that exhibited differential expression between the four studied cultivars, a group of miRNAs with a common expression pattern in all the analysed varieties caught our immediate attention. This group consisted of both conserved miRNAs (miR159, miR2111, miR396 and miR482) and legume‐specific miRNAs (miR10197 and miR1509). Among these, miR1509 and miR396 were found to be upregulated in trifoliates of chilled soybean plants at V1 stage, whereas miR10197, miR159, miR2111 and miR482 were downregulated. In the analysis of the degradome libraries, genes that may be the targets of these miRNAs were identified. These genes included pyruvate dehydrogenase, auxin signalling F‐box 2, aspartyl protease, TIM21‐like protein, TMV resistance protein N and vegetative storage protein. Such expression trends of these miRNAs suggest that their target genes play a universal role in controlling chilling stress response across different soybean cultivars. Most of the differentially expressed miRNAs reported in this study were found in the trifoliates. Expression profiles of some miRNAs showed specificity towards a particular tissue, in that they were found only in radicles, such as miR10190 and miR862, or in trifoliates, such as miR1511, miR168 and miR391. Interestingly, expressional patterns of other miRNAs such as miR1509, miR10440 or miR171 proved that chilling stress can cause one miRNA to be upregulated in one tissue but to be downregulated in another tissue. Differential expression of miRNA in various parts of soybean plants has been reported previously (Sun et al., 2016). Additionally, findings detailing the expression of miR157 in leaves and roots of Prunus persica (Eldem et al., 2012) support the existence of this phenomenon.Thirteen miRNAs were found to have contrasting trends of expression in Toyomusume and at least two other cultivars. These trends were observed mainly in trifoliates (V1 stage); however, the differential expression of most of these miRNAs was detected in all tissues in at least one cultivar. miR156 was found to be downregulated in Augusta, Fiskeby V and G. soja, whereas no significant change in expression was observed in Toyomusume. Interestingly, in all other cases, Fiskeby V and Toyomusume shared the expressional pattern of upregulation, as opposed to Augusta and G. soja, in which the said miRNAs were downregulated during chilling stress. An exception to this tendency was miR319, whose levels increased in Augusta and G. soja and decreased in Fiskeby V and Toyomusume under low‐temperature conditions. The legume‐specific miR1507, which was downregulated in trifoliates of Augusta and G. soja and upregulated in Fiskeby V and Toyomusume, was predicted to control the expression of aspartic proteinase, which plays a role in protein turnover and biotic stress tolerance in plants (Mazorra‐Manzano & Yada, 2008). Furthermore, two soybean‐specific miRNAs, namely miR3522 and miR4996, exhibited analogical expression patterns to miR1507. miR3522 was assigned to the CONSTANS‐LIKE 2B/A, which is involved in developmental processes, including flowering and root elongation (Steinbach, 2019). According to the degradome analysis, miR4996 potentially cleaves the transcripts of cysteine proteinase, which is responsible for the degradation of proteins from energetic reserves and proteins damaged due to stress conditions (Grudkowska & Zagdańska, 2004), and omega‐6 FAD, which regulates the content of unsaturated fatty acids in the plasma membrane (Dar et al., 2017). Furthermore, many genes predicted to be targeted by differentially expressed miRNAs in this study remain uncharacterized, which leaves much room for future advancement in elucidating chilling stress response in soybean.Annotation of differentially expressed conserved miRNAs with their putative target genesConversely, the annotation of differentially expressed conserved miRNAs with their putative target genes proved to be more fruitful (compared to legume/soybean‐specific miRNAs). miR156, which was downregulated in trifoliates of Augusta, Fiskeby V and G. soja but upregulated (not significantly) in Toyomusume, was predicted to control the expression of teosinte glume architecture 1 (TGA1), the transcriptional regulator belonging to the SBP (squamosa promoter binding‐like protein) family, which was found to play a role in transition from juvenile to adult stage in maize, where it was also shown to be targeted by miR156 (Studer et al., 2017). miR159, which was downregulated in trifoliates of Augusta and G. soja but upregulated in Fiskeby V and Toyomusume, was predicted to target the CDPK‐related kinase 6 (CRK6), which is reported to be involved in ROS metabolism and shown to have an extensive role in the abiotic stress tolerance response in Oryza sativa, Zea mays, Populus trichocarpa and Brassica napus (Bulgakov et al., 2011; Xiao et al., 2017). According to the study conducted on the vegetable soybean, the expression of miR159 was decreased due to chilling stress (Xu et al., 2016). miR166, having the same expressional pattern as miR159, was proposed to regulate ATHB‐14, ATHB‐15 and GAMYB. Another research group also reported ATHB‐14 as a target gene of miR166 in soybean (Li et al., 2017). In that study, the authors found members of the miR166 family to be responsive to cold stress (Li et al., 2017). In Camellia sinensis, several members of the miR166 family were downregulated under drought stress, and a negative correlation was observed between the expression of miR166 and ATHB‐14 like and ATHB‐ like (Guo et al., 2017). miR319 is known to target transcription factors from the family of TCP (TEOSINTE BRANCHED1/CYCLOIDEA/PCF) involved in leaf morphogenesis (Bresso et al., 2018). Our results showed that during chilling stress, miR319 was downregulated in roots of Fiskeby V and G. soja, with opposite tendencies in Toyomusume and Augusta. Furthermore, TCP2/3/4 were assigned as target genes of miR319 in the degradome analysis, which was corroborated by the work of another research group that described the relationship of miR319 and TCP3/4 in the context of flavonoid biosynthesis in soybean (Gupta et al., 2019). Another pattern of downregulation in Augusta and G. soja and upregulation in Fiskeby V and Toyomusume (not significantly) was observed for miR397 in trifoliates. Other authors reported that during water deficit in soybean, miR397ab showed downregulation in a resistant cultivar but upregulation in a sensitive cultivar (Kulcheski et al., 2011). Here, we predicted that NAC18/19 (Petunia No Apical Meristem (NAM), Arabidopsis transcription activation factors (ATAF1 and ATAF2), cup‐shaped cotyledon 2 (CUC2)) and laccase‐7 may be potential targets of miR397. NAC is a family of transcription factors involved in various developmental processes such as hormone signalling, fruit ripening and stress response (Hussain et al., 2017). According to Yang et al. (2019) NAC109 was upregulated in roots and shoots shortly after cold stress in soybean. Another research group reported that the expression of NAC19 was induced by ABA and JA, and it was engaged in the process of programmed cell death accompanied by ROS accumulation (Wang et al., 2015). Laccases in plants are mostly associated with the lignification process; however, they were also found to be involved in abiotic stress response in Arabidopsis (Wang et al., 2019). ROS are almost an inseparable factor of abiotic stress in plants; therefore, the capacity of any given organism to neutralize these pernicious molecules is essential for endurance of adverse conditions (Dar et al., 2017). Nature's response to ROS is the development of the antioxidative system, including copper/zinc SOD (Feng et al., 2016). Our data showed downregulation of miR398 in trifoliates of Augusta and G. soja, and the opposite expression pattern was observed in Fiskeby V and Toyomusume. Further analysis suggests that Cu/Zn SOD and CSS are the target genes of miR398. Previous studies have shown that miR398 downregulates the transcription of its target genes, namely CCS and Cu/Zn SODs (CSD1 and CSD2), in Arabidopsis, thus corroborating its role in stress regulation (Beauclair et al., 2010; Guan et al., 2013).Novel miRNA with differential expression under chilling in soybean and their target genesIn the present study, 18 novel miRNAs were found that exhibited significant differential expression between control and chilled samples. The majority of these miRNAs were identified in cotyledons and trifoliates of G. soja. This may be explained by G. soja having the largest difference in its genome as compared to other tested cultivars that belong to the genus Glycine. Further analysis enabled to classify the target genes for some of the novel miRNAs found responsive to chilling stress. For example, novel miR151035 targeted the scarecrow‐like (SCL6) protein in trifoliates of Fiskeby V. In Arabidopsis, SCL6 was found to coordinate the shoot branching process (Wang et al., 2010). Another study showed that SCL6 is involved in the nodulation process in soybean (Hossain et al., 2019). Novel miR119406 targeted ATP sulphurylase (ATPS) in trifoliates of G. soja. The expression of ATPS was observed to be induced due to cold treatment in soybean (Phartiyal et al., 2006). ATPS, as a crucial enzyme in the sulphur assimilation pathway, controls the rate of cysteine synthesis, which is one of the substrates of glutathione that plays a role in cold resistance of plants (Phartiyal et al., 2006). Interestingly, novel miR60377/64371/49212 targeted a protein containing F‐box/kelch repeat in trifoliates of G. soja and Augusta as well as in cotyledons of G. soja. F‐box proteins are a part of the E3 ubiquitin–protein ligase complex and were reported to be involved in the response to salinity, drought and heavy metal stress in Medicago truncatula (Song et al., 2015).Target gene expression analysis under chilling in soybeanThe analysis of expression levels of selected genes showed changes caused by cold stress; one of the genes is transcription factor NAC‐19 which belongs to one of the largest TF families that regulate plant growth, development and responses to environmental stresses (Diao et al., 2020; Zhang et al., 2018). In the present study, following chilling stress, NAC‐19 was significantly upregulated in roots and cotyledons of all tested varieties. This result correlated well with observed downregulation of miR408 in cotyledons which was predicted to target NAC‐19 (Table 4; Figure 6). To date, several reports indicated upregulation of many NAC TFs by cold stress in several different plant species like A. thaliana, Brassica napus, Capsicum annum L., Glycine max, Oryza sativa, Triticum aestivum, Zea maize and many more (reviewed in Diao et al., 2020).NADP dehydrogenases are key components of NADPH production systems necessary to maintain redox balance in the cells, and preserving redox homeostasis is especially important during stress exposure (Begara‐Morales et al., 2019; Sun et al., 2019; Wang et al., 2016). Malate dehydrogenase (GmMDH) belongs to a group of oxidoreductases that catalyse the conversion of malate and oxaloacetate, the reaction accompanied by reduction in the NAD(H) or NADP(H) pool. NADP is an important reducing agent for the synthesis of defensive substances and anabolic reactions (Sun et al., 2019; Wang et al., 2016). The role of MDH in stress responses has been proven, among others, in A. thaliana (Hebbelmann et al., 2012; Zhao et al., 2020), transgenic apple plants (Wang et al., 2016) and winter rye (Crecelius et al., 2003). Hence, it is not surprising that in roots of Augusta and Toyomusume varieties, GmMDH levels were upregulated, or maintained at the same level (in Fiskeby V). Only in one variety (G. soja), a decrease in the levels of GmMDH was observed. In cotyledons only, Augusta maintained increased expression level of GmMDH. An increase was also observed in G. soja. Phytocyanins (PCs) are ancient blue copper proteins which function as electron transporters. Previously it has been shown that PCs play important roles in cell differentiation and reorganization, organ development and also abiotic stress responses (Cao et al., 2015; Ma et al., 2011; Ruan et al., 2011). In the present study, an increase in the gene expression levels of Glycine max putative phytocyanin has been observed in roots of Augusta and Fiskeby V – the two cold‐resistant varieties. On the contrary, in cotyledons, the expression levels were decreased in Augusta, Fiskeby V and Toyomusume. The differential expression profiles of enzymes involved redox homeostasis and electron transport, which may be responsible to some extent for increased/decreased susceptibility to abiotic stresses. However, more in‐depth analyses are necessary to decipher the mechanism underlying the observed changes.Metabolic pathways affected in soybean under chillingIn the present study, we performed the GO analysis and KEGG pathway classification of genes predicted to be targeted by miRNAs that were differentially expressed under chilling stress. According to these analyses, terms that were highly represented included cellular nitrogen compound metabolism, cellular protein modification and stress response. These processes were strongly related to plant abiotic stress resistance. For instance, various protein modifications such as ubiquitination, sumoylation and phosphorylation activate numerous transcription factors crucial in abiotic stress response (Kosová et al., 2018). Furthermore, auxin‐activated signalling, regulation of transcription and secondary metabolism also constituted a substantial part of these classifications, suggesting a profound contribution in survival under low‐temperature conditions. It has been established that auxins assist plants in coping with environmental stresses by regulating transcription factors and modulating growth and development (Bielach et al., 2017). Furthermore, secondary metabolites such as flavonoids, isoprenes and cinnamic acid derivatives that are known to be overproduced due to chilling stress can neutralize ROS (Isah, 2019; Yang et al., 2018).It has been shown that reprogramming of the central carbohydrate metabolism plays a key role in cold acclimation in plants (Fürtauer et al., 2019; Hoermiller et al., 2017; Ritonga & Chen, 2020). These findings were corroborated in our study, as seen in the results of KEGG analysis, where it has been shown that sugar metabolism was among the pathways affected mostly by the 16 differentially expressed miRNAs. In Arabidopsis, starch metabolism is considered as a determinant of plant fitness under abiotic stress as it responds with great plasticity to various growth conditions. Additionally, various sugars stabilize biological membranes, liposomes, act as osmoprotectants or even stabilize photosynthesis during stress as the reduction in photosynthetic capacity is often accompanied by increased sugar accumulation (Fürtauer et al., 2019; Hajihashemi et al., 2018). Photosynthesis and CO2 fixation were also affected and negatively regulated by cold stress (Banerjee & Roychoudhury, 2019; Calzadilla et al., 2019; Hajihashemi et al., 2018), which was further confirmed in our studies, as enzymes involved in carbon fixation, photosynthesis and nicotinate and nicotinamide metabolism were among the targets of miRNAs with changed expression levels under cold stress. Glutathione, organic sulphur repository, in its reduced form (GSH) is an essential metabolite in various biosynthetic pathways like detoxification and redox homeostasis (Rao & Reddy, 2008). To date several reports indicated the involvement of glutathione in responses to abiotic stresses (Hasanuzzaman et al., 2017; Kocsy, Szalai, et al., 2000, Kocsy, Von Ballmoos, et al., 2000; Spanò et al., 2017). At low non‐freezing temperatures, high GSH content and glutathione reductase activity were detected in several plant species, indicating a possible contribution to chilling tolerance and cold acclimation (Kader et al., 2011). Glutathione metabolism was one of the pathways indicated in our KEGG analysis, where three enzymes: glutathione reductase (ec:1.8.4.2), 5‐oxoprolinase (ATP‐hydrolysing) (ec:3.5.2.9) and dehydrogenase (NADP+) (ec:1.1.1.49) were predicted as targets of miRNAs with changed expression under chilling. The classification of these metabolic changes may shed light on the role of target genes of differentially expressed miRNAs in stress responses of soybean.CONCLUSIONSCold stress is one of the major environmental factors that severely affects plant growth and development and negatively influences crop productivity. Some plants are able to cope with this stress and acquire chilling tolerance; in some species/varieties/single individuals, the exposure to this stress triggers developmental responses. As tender legumes, soybeans thrive in warm climates and are sensitive to cold. In the present study, to determine the involvement of miRNAs and their target genes in chilling resistance of four soybean cultivars varying in cold stress susceptibility, high‐throughput sequencing was used to identify cold‐responsive miRNAs and their target genes. A total of 321 known miRNAs were identified, and 348 novel miRNAs were predicted, of which 162 miRNAs, including well‐conserved, legume‐ and soybean‐specific miRNAs, and 18 novel miRNAs, respectively, had changed expression profiles. Interestingly, several miRNAs such as miR156, miR169 and miR5770 had similar expression patterns in Augusta, Fiskeby V and G. soja, which clearly contrasted from that in cold‐sensitive Toyomusume variety. Altogether, the results suggest that these miRNAs may play a role in the chilling responses of soybean. Degradome analysis as well as GO and KEGG annotations allowed us to assign potential target genes to the differentially expressed miRNAs. Many of these genes were found to be related to plant abiotic stress response mechanisms such as ROS scavenging, flavonoid biosynthesis and regulation of osmotic potential. In summary, our findings provide valuable insights into the function of miRNAs in the soybean chilling resistance and may provide crucial knowledge in the development of new cultivars. Investigating the molecular mechanisms of soybean chilling stress responses will facilitate better understanding of the response of plant species to chilling and help to reduce the consequences of this major environmental stress on plants.Sequence dataThese sequence data have been submitted to the Sequence Read Archive (SRA) BioProject (NCBI) databases under accession number PRJNA725380.ACKNOWLEDGEMENTSThe work was supported by a grant no. UMO‐2014/15/B/NZ9/02312 from the National Science Centre, Poland, and the Ministry of Science and Higher Education of the Republic of Poland via the KNOW program. We are thankful to Prof. J. Nawracała for providing the seeds of the soybean cultivars for the experiments.CONFLICT OF INTERESTThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.AUTHOR CONTRIBUTIONSA.T., J.G.‐B. and T.T. – conceptualization; A.T., J.G.‐B. and W.K. – methodology; A.T., J.G.‐B. and W.K. – validation; T.T. – formal analysis; J.K., A.T., J.G.‐B. and W.K. – investigation; T.T. – resources; A.T. and J.G.‐B. – data curation; J.K. – writing–original draft preparation; J.K., A.T., J.G.‐B., W.M.K. and T.T. – writing–editing; J.K. – visualization; A.T. – supervision; A.T. and J.G.‐B. – project administration; T.T. – funding acquisition. All authors have read and agreed to the published version of the manuscript.DATA AVAILABILITY STATEMENTData openly available in a public repository that does not issue DOIs. 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Journal

Journal of Agronomy and Crop Science – Wiley

Published: Dec 1, 2022

Keywords: chilling; cold stress; degradome; plant stress responses; small RNAs; soybean

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