Starch is a major component of crop grains, and its content affects food quality and taste. Tartary buckwheat is a traditional pseudo-cereal used in food as well as medicine. Starch content, granule morphology, and physicochemical properties have been extensively studied in Tartary buckwheat. However, the complex regulatory network related to its starch biosynthesis needs to be elucidated. Here, we performed RNA-seq analyses using seven Tartary buckwheat varieties differing in starch content and combined the RNA-seq data with starch content by weighted correlation network analysis (WGCNA). As a result, 10,873 differentially expressed genes (DEGs) were identified and were functionally clustered to six hierarchical clusters. Fifteen starch biosynthesis genes had higher expression level in seeds. Four trait-specific modules and 3131 hub genes were identified by WGCNA, with the lightcyan and brown modules positively correlated with starch-related traits. Furthermore, two potential gene regulatory networks were proposed, including the co-expression of FtNAC70, FtPUL, and FtGBSS1-3 in the lightcyan module and FtbHLH5, C3H, FtBE2, FtISA3, FtSS3-5, and FtSS1 in the brown. All the above genes were preferentially expressed in seeds, further suggesting their role in seed starch biosynthesis. These results provide crucial guidance for further research on starch biosynthesis and its regulatory network in Tartary buckwheat.

Plant NAC transcription factors play vital roles in diverse biological processes, especially in response to stress. However, only a limited number of the FtNAC genes have been cloned and functionally analyzed in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn.), which is a coarse grain crop that is highly resistant to abiotic stress. Here, we identified seven Tartary buckwheat FtNAC genes, namely FtNAC11, FtNAC12, FtNAC26, FtNAC28, FtNAC65, FtNAC70, and FtNAC78. Multiple-sequence alignments revealed that all of them contained the conserved NAM domain, which consisted of the A, B, C, D, and E subdomains. Seven NAC proteins were classified into six subgroups based on phylogenetic analyses. Cis-acting regulatory elements analyses (CARE) indicated that there were numerous CAREs related to biotic and abiotic responses and hormone responses in the promoters of FtNAC genes. qRT-PCR showed that they were tissue-specifically expressed and could be induced by four out of six treatments, namely PEG, NaCl, cold, MeJA, ABA, and GA, implying that they were abiotic and hormone responsive. Among these, FtNAC70 was the most strongly induced gene and could be induced by all six treatments. Meanwhile, FtNAC70-overexpressed Arabidopsis showed more resistance to salt and drought. The identification, phylogenetics, expression, and functional study of seven stress-responsive FtNAC genes laid a foundation for further research on the molecular mechanism of abiotic stress regulated by NAC TFs in Tartary buckwheat.

Anthocyanin is one important nutrition composition in Tartary buckwheat (Fagopyrum tataricum) sprouts, a component missing in its seeds. Although anthocyanin biosynthesis requires light, the mechanism of light-induced anthocyanin accumulation in Tartary buckwheat is unclear. Here, comparative transcriptome analysis of Tartary buckwheat sprouts under light and dark treatments and biochemical approaches were performed to identify the roles of one B-box protein BBX22 and ELONGATED HYPOCOTYL 5 (HY5). The overexpression assay showed that FtHY5 and FtBBX22 could both promote anthocyanin synthesis in red-flower tobacco. Additionally, FtBBX22 associated with FtHY5 to form a complex that activates the transcription of MYB transcription factor genes FtMYB42 and FtDFR, leading to anthocyanin accumulation. These findings revealed the regulation mechanism of light-induced anthocyanin synthesis and provide excellent gene resources for breeding high-quality Tartary buckwheat.

According to the requirements of Tartary buckwheat breeding, it is necessary to develop a method for the rapid detection of functional substances in seeds. To ensure a diverse sample pool, we utilized the stable recombinant inbred lines (RILs) of Tartary buckwheat. The coefficients of variation of the total flavonoid, vitamin E (VE), and GABA contents of the RIL population were 15.06, 16.53, and 36.93, respectively. Subsequently, we established prediction models for the functional substance contents in Tartary buckwheat using near-infrared spectroscopy (NIRS) combined with chemometrics. The Kennard–Stone algorithm divided the dataset into training and test sets, employing six different methods for preprocessing spectra. The Competitive Adaptive Reweighted Sampling algorithm extracted the characteristic spectra. The best models for total flavonoid and VE were normalized using the first derivative. The calibration correlation coefficient (Rc) and prediction correlation coefficient (Rp) of the total flavonoid and VE prediction models were greater than 0.94. The optimal GABA prediction model underwent preprocessing via normalization combined with the standard normal variate, and the Rc and Rp values were greater than 0.93. The results demonstrated that the NIRS-based prediction model could satisfy the requirements for the rapid determination of total flavonoids, VE, and GABA in Tartary buckwheat seeds.

To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm⁻¹ was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R²), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R², SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm⁻¹ was suitable for modeling (calibration sets: validation set = 4:1), the R², SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R², SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method.