Weighted gene co-expression network analysis unveils gene networks regulating folate biosynthesis in maize endosperm.

Folates are essential elements for human growth and development, and their deficiency can lead to serious disorders. Waxy maize is a rich source of folates; however, the regulatory mechanism underlying folate biosynthesis in the endosperm remains unclear. Here, we examined changes in the folate content of maize endosperm collected at 15, 18, 21, 24, and 27 days after pollination (DAP) using liquid chromatograph-mass spectrometry and identified genes related to folate biosynthesis using transcriptome sequencing data. The results showed that 5-methyl-tetrahydrofolate and 5,10-methylene tetrahydrofolate were the main storage forms of folates in the endosperm, and their contents were relatively high at 21-24 days. We also identified 569, 3183, 4365, and 5513 differentially expressed genes (DEGs) in different days around milk stage. Functional annotation revealed 518 transcription factors (TFs) belonging to 33 families exhibiting specific expression in at least one sampling time. The key hub genes involved in folate biosynthesis were identified by weighted gene co-expression network analysis. In total, 24,976 genes were used to construct a co-expression network with 29 co-expression modules, among which the brown and purple modules were highly related to folate biosynthesis. Further, 187 transcription factors in the brown and purple modules were considered potential transcription factors related to endosperm folate biosynthesis. These results may improve the understanding of the molecular mechanism underlying folate biosynthesis in waxy maize and lead to the development of nutritionally fortified varieties. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02974-7.

Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding.

Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.

The dynamic transcriptome of waxy maize (Zea mays L. sinensis Kulesh) during seed development.

BACKGROUND: Waxy maize (Zea mays L. sinensis Kulesh) is a mutant of maize (Zea mays L.) with a mutation at Waxy1 (Wx1) gene locus. The seed of waxy maize has higher viscosity compared to regular maize. By now, we know little about the expression patterns of genes that involved in the seed development of waxy maize. OBJECTIVE: By analyzing the transcriptome data during waxy maize seed development, we attempt to dig out the genes that may influence the seed development of waxy maize. METHODS: The seeds of waxy maize inbred line SWL01 from six phases after pollination were used to do RNA-seq. Bioinformatics methods were used to analyze the expression patterns of the expressed genes, to identify the genes involved in waxy maize seed development. RESULTS: A total of 24,546 genes including 1611 transcription factors (TFs) were detected during waxy maize seed development. Coexpression analysis of expressed genes revealed the dynamic processes of waxy maize seed development. Particularly, 2457 genes including 177 TFs were specially expressed in waxy maize seed, some of which mainly involved in the process of seed dormancy and maturation. In addition, 2681, 5686, 4491, 4386, 3669 and 4624 genes were identified to be differential expressed genes (DEGs) at six phases compared to regular maize B73, and 113 DEGs among them may be key genes that lead the difference of seed development between waxy and regular maizes in milk stage. CONCLUSION: In summary, we elucidated the expression patterns of expressed genes during waxy maize seed development globally. A series of genes that associated with seed development were identified in our research, which may provide an important resource for functional study of waxy maize seed development to help molecular assisted breeding.

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing.

KEY MESSAGE: Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers.

Enriching of kernel zinc (Zn) concentration in maize is one of the most effective ways to solve the problem of Zn deficiency in low and middle income countries where maize is the major staple food, and 17% of the global population is affected with Zn deficiency. Genomic selection (GS) has shown to be an effective approach to accelerate genetic gains in plant breeding. In the present study, an association-mapping panel and two maize double-haploid (DH) populations, both genotyped with genotyping-by-sequencing (GBS) and repeat amplification sequencing (rAmpSeq) markers, were used to estimate the genomic prediction accuracy of kernel Zn concentration in maize. Results showed that the prediction accuracy of two DH populations was higher than that of the association mapping population using the same set of markers. The prediction accuracy estimated with the GBS markers was significantly higher than that estimated with the rAmpSeq markers in the same population. The maximum prediction accuracy with minimum standard error was observed when half of the genotypes were included in the training set and 3,000 and 500 markers were used for prediction in the association mapping panel and the DH populations, respectively. Appropriate levels of minor allele frequency and missing rate should be considered and selected to achieve good prediction accuracy and reduce the computation burden by balancing the number of markers and marker quality. Training set development with broad phenotypic variation is possible to improve prediction accuracy. The transferability of the GS models across populations was assessed, the prediction accuracies in a few pairwise populations were above or close to 0.20, which indicates the prediction accuracies across years and populations have to be assessed in a larger breeding dataset with closer relationship between the training and prediction sets in further studies. GS outperformed MAS (marker-assisted-selection) on predicting the kernel Zn concentration in maize, the decision of a breeding strategy to implement GS individually or to implement MAS and GS stepwise for improving kernel Zn concentration in maize requires further research. Results of this study provide valuable information for understanding how to implement GS for improving kernel Zn concentration in maize.

Isolation and characterization of formaldehyde-degrading fungi and its formaldehyde metabolism.

Formaldehyde is classified as a human carcinogen that may cause nasopharyngeal cancer and probably leukemia. The effects of environmental and nutritional factors on fungal growth and the biodegradation of formaldehyde were investigated. Fungal strains SGFA1 and SGFA3 isolated from untreated sewage sediment samples collected from heavily formaldehyde-contaminated areas were identified using morphological characteristics and molecular techniques and named as Aspergillus nomius SGFA1 and Penicillium chrysogenum SGFA3. Results indicate that SGFA1 and SGFA3 completely consumed 3,000 and 900 mg l(-1) of formaldehyde, respectively, within 7 days under optimized conditions. Quantitative real-time PCR analyses and enzyme activity analyses demonstrated that glutathione-dependent formaldehyde dehydrogenase (GDFADH) and formate dehydrogenase (FDH) pathway may play a functional role in enhancing formaldehyde-degrading capability in SGFA1. Both fungi have potential use for remediation of formaldehyde pollution.

Genome-wide survey and expression analysis of the MADS-box gene family in soybean.

MADS-box genes encode important transcription factors in plants that are involved in many processes during plant growth and development. An investigation of the soybean genome revealed 106 putative MADS-box genes. These genes were classified into two classes, type I and type II, based on phylogenetic analysis. The soybean type II group has 72 members, which is higher than that of Arabidopsis, indicating that soybean type II genes have undergone a higher rate of duplication and/or a lower rate of gene loss after duplication. Soybean MADS-box genes are present on all chromosomes. Like Arabidopsis and rice MADS-box genes, soybean MADS-box genes expanded through tandem gene duplication and segmental duplication events. There are many duplicate genes distributed across the soybean genome, with two genomic regions, i.e., MADS-box gene hotspots, where MADS-box genes with high degrees of similarity are clustered. Analysis of high-throughput sequencing data from soybean at different developmental stages and in different tissues revealed that MADS-box genes are expressed in embryos of various stages and in floral buds. This expression pattern suggests that soybean MADS-box genes play an important role in soybean growth and floral development.

Overexpression of AtFRO6 in transgenic tobacco enhances ferric chelate reductase activity in leaves and increases tolerance to iron-deficiency chlorosis.

The Arabidopsis gene FRO6(AtFRO6) encodes ferric chelate reductase and highly expressed in green tissues of plants. We have expressed the gene AtFRO6 under the control of a 35S promoter in transgenic tobacco plants. High-level expression of AtFRO6 in transgenic plants was confirmed by northern blot analysis. Ferric reductase activity in leaves of transgenic plants grown under iron-sufficient or iron-deficient conditions is 2.13 and 1.26 fold higher than in control plants respectively. The enhanced ferric reductase activity led to increased concentrations of ferrous iron and chlorophyll, and reduced the iron deficiency chlorosis in the transgenic plants, compared to the control plants. In roots, the concentration of ferrous iron and ferric reductase activity were not significantly different in the transgenic plants compared to the control plants. These results suggest that FRO6 functions as a ferric chelate reductase for iron uptake by leaf cells, and overexpression of AtFRO6 in transgenic plants can reduce iron deficiency chlorosis.