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Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

Improving nitrogen use efficiency (NUE) is very important for crops throughout the world. Rice mainly utilizes ammonium as an N source, but it also has four NRT2 genes involved in nitrate transport. The OsNRT2.3b transporter is important for maintaining cellular pH under mixed N supplies. Overexpression of this transporter driven by a ubiquitin promoter in rice greatly improved yield and NUE. This strategy for improving the NUE of crops may also be important for other cereals such as wheat and barley, which also face the challenges of nutrient uptake balance. To test this idea, we constructed transgenic barley lines overexpressing OsNRT2.3b. These transgenic barley lines overexpressing the rice transporter exhibited improved growth, yield, and NUE. We demonstrated that NRT2 family members and the partner protein HvNAR2.3 were also up-regulated by nitrate treatment (0.2 mM) in the transgenic lines. This suggests that the expression of OsNRT2.3b and other HvNRT2 family members were all up-regulated in the transgenic barley to increase the efficiency of N uptake and usage. We also compared the ubiquitin (Ubi) and a phloem-specific (RSs1) promoter-driven expression of OsNRT2.3b. The Ubi promoter failed to improve nutrient uptake balance, whereas the RSs1 promoter succeed in increasing the N, P, and Fe uptake balance. The nutrient uptake enhancement did not include Mn and Mg. Surprisingly, we found that the choice of promoter influenced the barley phenotype, not only increasing NUE and grain yield, but also improving nutrient uptake balance.

Improving nitrogen use efficiency (NUE) is very important for crops throughout the world. Rice mainly utilizes ammonium as an N source, but it also has four NRT2 genes involved in nitrate transport. The OsNRT2.3b transporter is important for maintaining cellular pH under mixed N supplies. Overexpression of this transporter driven by a ubiquitin promoter in rice greatly improved yield and NUE. This strategy for improving the NUE of crops may also be important for other cereals such as wheat and barley, which also face the challenges of nutrient uptake balance. To test this idea, we constructed transgenic barley lines overexpressing OsNRT2.3b. These transgenic barley lines overexpressing the rice transporter exhibited improved growth, yield, and NUE. We demonstrated that NRT2 family members and the partner protein HvNAR2.3 were also up-regulated by nitrate treatment (0.2 mM) in the transgenic lines. This suggests that the expression of OsNRT2.3b and other HvNRT2 family members were all up-regulated in the transgenic barley to increase the efficiency of N uptake and usage. We also compared the ubiquitin (Ubi) and a phloem-specific (RSs1) promoter-driven expression of OsNRT2.3b. The Ubi promoter failed to improve nutrient uptake balance, whereas the RSs1 promoter succeed in increasing the N, P, and Fe uptake balance. The nutrient uptake enhancement did not include Mn and Mg. Surprisingly, we found that the choice of promoter influenced the barley phenotype, not only increasing NUE and grain yield, but also improving nutrient uptake balance.

Borna disease virus (BDV) persists in the central nervous systems of a wide variety of vertebrates and causes behavioral disorders. Previous studies have revealed that metabolic perturbations are associated with BDV infection. However, the pathophysiological effects of different viral strains remain largely unknown. Rat cortical neurons infected with human strain BDV Hu-H1, laboratory BDV Strain V, and non-infected control (CON) cells were cultured in vitro. At day 12 post-infection, a gas chromatography coupled with mass spectrometry (GC–MS) metabonomic approach was used to differentiate the metabonomic profiles of 35 independent intracellular samples from Hu-H1-infected cells (n = 12), Strain V-infected cells (n = 12), and CON cells (n = 11). Partial least squares discriminant analysis (PLS-DA) was performed to demonstrate discrimination between the three groups. Further statistical testing determined which individual metabolites displayed significant differences between groups. PLS-DA demonstrated that the whole metabolic pattern enabled statistical discrimination between groups. We identified 31 differential metabolites in the Hu-H1 and CON groups (21 decreased and 10 increased in Hu-H1 relative to CON), 35 differential metabolites in the Strain V and CON groups (30 decreased and 5 increased in Strain V relative to CON), and 21 differential metabolites in the Hu-H1 and Strain V groups (8 decreased and 13 increased in Hu-H1 relative to Strain V). Comparative metabonomic profiling revealed divergent perturbations in key energy and amino acid metabolites between natural strain Hu-H1 and laboratory Strain V of BDV. The two BDV strains differentially alter metabolic pathways of rat cortical neurons in vitro. Their systematic classification provides a valuable template for improved BDV strain definition in future studies.

Endogenous bornavirus-like nucleoprotein elements (EBLNs) have been discovered in the genomes of various animals including humans, whose functions have been seldom studied. To explore the biological functions of human EBLNs, we constructed a lentiviral vector expressing a short-hairpin RNA against human EBLN1, which successfully inhibited EBLN1 expression by above 80% in infected human oligodendroglia cells (OL cells). We found that EBLN1 silencing suppressed cell proliferation, induced G2/M phase arrest, and promoted apoptosis in OL cells. Gene expression profiling demonstrated that 1067 genes were up-regulated, and 2004 were down-regulated after EBLN1 silencing. The top 10 most upregulated genes were PI3, RND3, BLZF1, SOD2, EPGN, SBSN, INSIG1, OSMR, CREB3L2, and MSMO1, and the top 10 most-downregulated genes were KRTAP2-4, FLRT2, DIDO1, FAT4, ESCO2, ZNF804A, SUV420H1, ZC3H4, YAE1D1, and NCOA5. Pathway analysis revealed that these differentially expressed genes were mainly involved in pathways related to the cell cycle, the mitogen-activated protein kinase pathway, p53 signaling, and apoptosis. The gene expression profiles were validated by using quantitative reverse transcription polymerase chain reaction (RT-PCR) for detecting these 20 most-changed genes. Three genes closely related to glioma, RND3, OSMR, and CREB3L2, were significantly upregulated and might be the key factors in EBLN1 regulating the proliferation and apoptosis of OL cells. This study provides evidence that EBLN1 plays a key role in regulating cell life and death, thereby opening several avenues of investigation regarding EBLN1 in the future.

Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), is a prescribed and effective antidepressant and generally used for the treatment of depression. Previous studies have revealed that the antidepressant mechanism of fluoxetine was related to astrocytes. However, the therapeutic mechanism underlying its mode of action in astrocytes remains largely unclear. In this study, primary astrocytes were exposed to 10 µM fluoxetine, 24 h post-treatment, a high-resolution proton nuclear magnetic resonance (1H NMR)-based metabolomic approach coupled with multivariate statistical analysis was used to characterize the metabolic variations of intracellular metabolites. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots of the spectra demonstrated that the fluoxetine-treated astrocytes were significantly distinguished from the untreated controls. In total, 17 differential metabolites were identified to discriminate the two groups. These key metabolites were mainly involved in lipids, lipid metabolism-related molecules and amino acids. This is the first study to indicate that fluoxetine may exert antidepressant action by regulating the astrocyte’s lipid and amino acid metabolism. These findings should aid our understanding of the biological mechanisms underlying fluoxetine therapy.