Effects of Elevated Aluminum Concentration and Distribution on Root Damage, Cell Wall Polysaccharides, and Nutrient Uptake in Different Tolerant Eucalyptus Clones.

Aluminized acidic soil can damage Eucalyptus roots and limit tree growth, hindering the productivity of Eucalyptus plantations. At present, the negative impacts of elevated aluminum (Al) on the cell morphology and cell wall properties of Eucalyptus root tip are still unclear. In order to investigate the responses of two different tolerant clones, Eucalyptus urophylla (G4) and Eucalyptus grandis × Eucalyptus urophylla (G9), to Al toxicity, seedling roots were treated hydroponically with an Al solution, and the polysaccharide content in the root tip cell wall and the characteristics of programmed cell death were studied. The results show that the distribution of Al was similar in both clones, although G9 was found to be more tolerant to Al toxicity than G4. The Al3+ uptake of pectin in root tip cell walls was significantly higher in G4 than in G9. The root tip in G4 was obviously damaged, enlarged, thickened, and shorter; the root crown cells were cracked and fluffy; and the cell elongation area was squeezed. The lower cell wall polysaccharide content and PME activity may result in fewer carboxylic groups in the root tip cell wall to serve as Al-binding sites, which may explain the stronger Al resistance of G9 than G4. The uptake of nitrogen and potassium in G4 was significantly reduced after aluminum application and was lower than in G9. Al-resistant Eucalyptus clones may have synergistic pleiotropic effects in resisting high aluminum-low phosphorus stress, and maintaining higher nitrogen and potassium levels in roots may be an important mechanism for effectively alleviating Al toxicity.

Metabolic acclimation supports higher aluminium-induced secretion of citrate and malate in an aluminium-tolerant hybrid clone of Eucalyptus.

BACKGROUND: Eucalyptus is the main plantation wood species, mostly grown in aluminized acid soils. To understand the response of Eucalyptus clones to aluminum (Al) toxicity, the Al-tolerant Eucalyptus grandis × E. urophylla clone GL-9 (designated "G9") and the Al-sensitive E. urophylla clone GL-4 (designated "W4") were employed to investigate the production and secretion of citrate and malate by roots. RESULTS: Eucalyptus seedlings in hydroponics were exposed to the presence or absence of 4.4 mM Al at pH 4.0 for 24 h. The protein synthesis inhibitor cycloheximide (CHM) and anion channel blocker phenylglyoxal (PG) were applied to explore possible pathways involved in organic acid secretion. The secretion of malate and citrate was earlier and greater in G9 than in W4, corresponding to less Al accumulation in G9. The concentration of Al in G9 roots peaked after 1 h and decreased afterwards, corresponding with a rapid induction of malate secretion. A time-lag of about 6 h in citrate efflux in G9 was followed by robust secretion to support continuous Al-detoxification. Malate secretion alone may alleviate Al toxicity because the peaks of Al accumulation and malate secretion were simultaneous in W4, which did not secrete appreciable citrate. Enhanced activities of citrate synthase (CS) and phosphoenolpyruvate carboxylase (PEPC), and reduced activities of isocitrate dehydrogenase (IDH), aconitase (ACO) and malic enzyme (ME) were closely associated with the greater secretion of citrate in G9. PG effectively inhibited citrate and malate secretion in both Eucalyptus clones. CHM also inhibited malate and citrate secretion in G9, and citrate secretion in W4, but notably did not affect malate secretion in W4. CONCLUSIONS: G9 immediately secrete malate from roots, which had an initial effect on Al-detoxification, followed by time-delayed citrate secretion. Pre-existing anion channel protein first contributed to malate secretion, while synthesis of carrier protein appeared to be needed for citrate excretion. The changes of organic acid concentrations in response to Al can be achieved by enhanced CS and PEPC activities, but was supported by changes in the activities of other enzymes involved in organic acid metabolism. The above information may help to further explore genes related to Al-tolerance in Eucalyptus.

Synergistic variation of rhizosphere soil phosphorus availability and microbial diversity with stand age in plantations of the endangered tree species Parashorea chinensis.

Introduction: Soil physicochemical properties and nutrient composition play a significant role in shaping microbial communities, and facilitating soil phosphorus (P) transformation. However, studies on the mechanisms of interactions between P transformation characteristics and rhizosphere microbial diversity in P-deficient soils on longer time scales are still limited. Methods: In this study, rhizosphere soils were collected from a pure plantation of Parashorea chinensis (P. chinensis) at six stand ages in the subtropical China, and the dynamic transformation characteristics of microbial diversity and P fractions were analyzed to reveal the variation of their interactions with age. Results: Our findings revealed that the rhizosphere soils across stand ages were in a strongly acidic and P-deficient state, with pH values ranging from 3.4 to 4.6, and available P contents ranging from 2.6 to 7.9 mg·kg-1. The adsorption of P by Fe3+ and presence of high levels of steady-state organic P highly restricted the availability of P in soil. On long time scales, acid phosphatase activity and microbial biomass P were the main drivers of P activation. Moreover, pH, available P, and ammonium nitrogen were identified as key factors driving microbial community diversity. As stand age increased, most of the nutrient content indicators firstly increased and then decreased, the conversion of other forms of P to bio-available P became difficult, P availability and soil fertility began to decline. However, bacteria were still able to maintain stable species abundance and diversity. In contrast, stand age had a greater effect on the diversity of the fungal community than on the bacteria. The Shannon and Simpson indices varied by 4.81 and 0.70 for the fungi, respectively, compared to only 1.91 and 0.06 for the bacteria. Microorganisms play a dominant role in the development of their relationship with soil P. Discussion: In conclusion, rhizosphere microorganisms in P. chinensis plantations gradually adapt to the acidic, low P environment over time. This adaptation is conducive to maintaining P bioeffectiveness and alleviating P limitation.

Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints.

The powerful CRISPR genome editing system is hindered by its off-target effects, and existing computational tools achieved limited performance in genome-wide off-target prediction due to the lack of deep understanding of the CRISPR molecular mechanism. In this study, we propose to incorporate molecular dynamics (MD) simulations in the computational analysis of CRISPR system, and present CRISOT, an integrated tool suite containing four related modules, i.e., CRISOT-FP, CRISOT-Score, CRISOT-Spec, CRISORT-Opti for RNA-DNA molecular interaction fingerprint generation, genome-wide CRISPR off-target prediction, sgRNA specificity evaluation and sgRNA optimization of Cas9 system respectively. Our comprehensive computational and experimental tests reveal that CRISOT outperforms existing tools with extensive in silico validations and proof-of-concept experimental validations. In addition, CRISOT shows potential in accurately predicting off-target effects of the base editors and prime editors, indicating that the derived RNA-DNA molecular interaction fingerprint captures the underlying mechanisms of RNA-DNA interaction among distinct CRISPR systems. Collectively, CRISOT provides an efficient and generalizable framework for genome-wide CRISPR off-target prediction, evaluation and sgRNA optimization for improved targeting specificity in CRISPR genome editing.

Personalized tumor combination therapy optimization using the single-cell transcriptome.

BACKGROUND: The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. METHODS: Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. RESULTS: We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. CONCLUSIONS: Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .