Identification of functional single nucleotide polymorphism of Populus trichocarpa PtrEPSP-TF and determination of its transcriptional effect

Accelerating wood domestication in forest trees through genome editing: Advances and prospects

The high economic value of wood requires intensive breeding towards multipurpose biomass. However, long breeding cycles hamper the fast development of novel tree varieties that have improved biomass properties, are tolerant to biotic and abiotic stresses, and resilient to climate change. To speed up domestication, the integration of conventional breeding and new breeding techniques is needed. In this review, we discuss recent advances in genome editing and Cas-DNA-free genome engineering of forest trees, and briefly discuss how multiplex editing combined with multi-omics approaches can accelerate the genetic improvement of forest trees, with a focus on wood.

Lignin engineering in forest trees: From gene discovery to field trials

Wood is an abundant and renewable feedstock for the production of pulp, fuels, and biobased materials. However, wood is recalcitrant toward deconstruction into cellulose and simple sugars, mainly because of the presence of lignin, an aromatic polymer that shields cell-wall polysaccharides. Hence, numerous research efforts have focused on engineering lignin amount and composition to improve wood processability. Here, we focus on results that have been obtained by engineering the lignin biosynthesis and branching pathways in forest trees to reduce cell-wall recalcitrance, including the introduction of exotic lignin monomers. In addition, we draw general conclusions from over 20 years of field trial research with trees engineered to produce less or altered lignin. We discuss possible causes and solutions for the yield penalty that is often associated with lignin engineering in trees. Finally, we discuss how conventional and new breeding strategies can be combined to develop elite clones with desired lignin properties. We conclude this review with priorities for the development of commercially relevant lignin-engineered trees.

Association between functional genetic variants in retinoid X receptor-α/γ and the risk of gestational diabetes mellitus in a southern Chinese population

To clarify the effect of retinoid X receptor-α/γ (RXR-α/γ) genes functional genetic variants (RXR-α rs4842194 G>A, RXR-γ rs100537 A>G and rs2134095 T>C) on the risk of gestational diabetes mellitus (GDM), a case–control study with 573 GDM patients and 740 pregnant women with normal glucose tolerance was performed in Guangxi area of China. An odds ratio (OR) with its corresponding 95% confidence interval (CI) was used to assess the strengths of the association between genetic variation and GDM. After adjustment of age and pre-BMI, the logistic regression analysis showed that the rs2134095 was significantly associated with GDM risk (CC vs. TT/TC: adjusted OR = 0.71, 95% CI = 0.56–0.90) in all subjects, and this result remained highly significant after Bonferroni’s correction for multiple testing (P=0.004). The stratified analysis showed that rs2134095 was significantly associated with the risk of GDM among age > 30 years (adjusted OR = 0.61, 95% CI = 0.39–0.97), BMI > 22 kg/m² (adjusted OR = 0.46, 95% CI = 0.30–0.70), systolic blood pressure (SBP) > 120 mmHg (adjusted OR = 1.96, 95% CI = 1.14–3.36), glycosylated hemoglobin A1c (HbA1c) < 6.5% (adjusted OR = 1.41, 95% CI = 1.11–1.78), TG ≤ 1.7 mmol/l (adjusted OR = 2.57, 95% CI = 1.45–4.53), TC ≤ 5.18 mmol/l (adjusted OR = 1.58, 95% CI = 1.13–2.22), high-density lipoprotein cholesterol (HDL-c) ≤ 1.5 mmol/l (adjusted OR = 1.70, 95% CI = 1.16–2.49) and low-density lipoprotein cholesterol (LDL-c) > 3.12 mmol/l (adjusted OR = 1.47, 95% CI = 1.08–2.00) subjects, under the recessive genetic model. We also found that rs2134095 interacted with age (P_interaction=0.039), pre-BMI (P_interaction=0.040) and TG (P_interaction=0.025) influencing individual’s genetic susceptibility to GDM. The rs2134095 T>C is significantly associated with the risk of GDM by effect of a single locus and/or complex joint gene–gene and gene–environment interactions. Larger sample-size and different population studies are required to confirm the findings.