CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.

BACKGROUND: Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC. METHOD: Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application. RESULTS: A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC. CONCLUSION: The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients. METHODS: Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis. RESULTS: We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC. CONCLUSION: We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

Proteomics Analysis Revealed Smad3 as a Potential Target of the Synergistic Antitumor Activity of Disulfiram and Cisplatin in Ovarian Cancer.

INTRODUCTION: Among gynecological cancers, ovarian cancer has a high mortality rate. Cisplatin-based chemotherapy is commonly used for the treatment of ovarian cancer. However, the clinical efficacy of cisplatin in ovarian cancer is limited due to the development of chemo-resistance during treatment. OBJECTIVE: In the study, we aimed to investigate the synergistic anti-cancer activity and targets of the FDA-approved drug disulfiram combined with cisplatin in ovarian cancer. METHODS: The cell viability was determined by Celltier-Glo luminescent assay. The synergistic anti-cancer activity was assessed by combination index. Cell cycle and apoptosis were detected by flow cytometry. The in vivo anti-tumor activity and side effects were evaluated using a xenografted mice model. The synergistic anti-cancer targets were identified by a mass spectrometry-based proteomics analysis. RESULTS: In this study, we first found that disulfiram synergistically enhanced the anti-tumor activity of cisplatin in chemo-resistant ovarian cancer cells, which was accompanied by the enhanced induction of cellular apoptosis. Secondly, the in vivo study demonstrated that the combination treatment of disulfiram and cisplatin dramatically inhibited tumor growth and had no apparent side effects in ovarian cancer xenografted mice. Finally, proteomics analysis identified SMAD3 as a potential target of disulfiram-cisplatin combined treatment, and the down-regulation of SMAD3 could increase cisplatin-induced cell death in ovarian cancer. CONCLUSION: Combination treatment of disulfiram and cisplatin synergistically inhibited the growth of ovarian cancer through down-regulating SMAD3. As a repurposed drug, disulfiram could be quickly transformed into a clinic to overcome cisplatin resistance for the treatment of ovarian cancer.

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA.

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

Association of the tissue infiltrated and peripheral blood immune cell subsets with response to radiotherapy for rectal cancer.

BACKGROUND: Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer. METHODS: In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution. RESULTS: Compared with the non-responsive group, the responsive group showed higher proportions of CD4+ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8+ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4+/CD8+ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4+ T cells and higher ratio of CD4+/CD8+ T cells, and lower proportion of CD8+ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632-0.857, better than the gene signatures. CONCLUSIONS: Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer.

Plin2-mediated lipid droplet mobilization accelerates exit from pluripotency by lipidomic remodeling and histone acetylation.

Metabolic switch is critical for cell fate determination through metabolic functions, epigenetic modifications, and gene expression. However, the mechanisms underlying these alterations and their functional roles remain unclear. Here, we show that Plin2-mediated moderate lipid hydrolysis is critical for pluripotency of embryonic stem cells (ESCs). Upon exit from pluripotency, lipid droplet (LD)-associated protein Plin2 is recognized by Hsc70 and degraded via chaperone-mediated autophagy to facilitate LD mobilization. Enhancing lipid hydrolysis by Plin2 knockout promotes pluripotency exit, which is recovered by ATGL inhibition. Mechanistically, excessive lipid hydrolysis induces a dramatic lipidomic remodeling characterized by decreased cardiolipin and phosphatidylethanolamine, which triggers defects in mitochondrial cristae and fatty acid oxidation, resulting in reduced acetyl-CoA and histone acetylation. Our results reveal how LD mobilization is regulated and its critical role in ESC pluripotency, and indicate the mechanism linking LD homeostasis to mitochondrial remodeling and epigenetic regulation, which might shed light on development and diseases.

Repurposing Disulfiram as a Chemo-Therapeutic Sensitizer: Molecular Targets and Mechanisms.

Currently, chemotherapy is still the main strategy for cancer treatment. However, chemotherapy resistance remains a challenge. Disulfiram (DSF) is an FDA-approved medicine for the treatment of alcoholism; however, it was later revealed to have anticancer properties. Importantly, numerous studies have shown that DSF can be employed as a chemotherapeutic sensitizer to enhance the anticancer efficacy of chemo-drugs in a variety of cancers. Furthermore, the combinations of DSF and chemo-drugs have been tested in clinical trials. In the review, we summarized the possible molecular targets and mechanisms of DSF to reverse chemo-resistance. We also further discussed the opportunities and challenges of DSF as a chemo-therapeutic sensitizer. In conclusion, DSF could be a potentially repurposed drug that sensitizes cancer cells to chemotherapy in the clinic.

Mitolysosome exocytosis, a mitophagy-independent mitochondrial quality control in flunarizine-induced parkinsonism-like symptoms.

Mitochondrial quality control plays an important role in maintaining mitochondrial homeostasis and function. Disruption of mitochondrial quality control degrades brain function. We found that flunarizine (FNZ), a drug whose chronic use causes parkinsonism, led to a parkinsonism-like motor dysfunction in mice. FNZ induced mitochondrial dysfunction and decreased mitochondrial mass specifically in the brain. FNZ decreased mitochondrial content in both neurons and astrocytes, without affecting the number of nigral dopaminergic neurons. In human neural progenitor cells, FNZ also induced mitochondrial depletion. Mechanistically, independent of ATG5- or RAB9-mediated mitophagy, mitochondria were engulfed by lysosomes, followed by a vesicle-associated membrane protein 2- and syntaxin-4-dependent extracellular secretion. A genome-wide CRISPR knockout screen identified genes required for FNZ-induced mitochondrial elimination. These results reveal not only a previously unidentified lysosome-associated exocytosis process of mitochondrial quality control that may participate in the FNZ-induced parkinsonism but also a drug-based method for generating mitochondria-depleted mammal cells.

Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non-invasive prediction model that had good ability to distinguish malignant pulmonary nodules.

Liquid biopsies to distinguish malignant from benign pulmonary nodules.

Over the past decades, low-dose computed tomography (LD-CT) screening has been widely used for the early detection of lung cancer. Increasing numbers of indeterminate pulmonary nodules are now being discovered. However, it remains challenging to distinguish malignant from benign pulmonary nodules, especially those considered to be small or ground-glass (GGN) nodules. Liquid biopsies have been successfully applied in the diagnosis of advanced lung cancer, and the potential value for early detection of lung cancer has made great progress. Recent studies have demonstrated the value of various blood-based tumor biomarkers in determining the nature of pulmonary nodules, including cell-free DNA (cfDNA), microRNAs (miRNAs), circulating tumor cells (CTCs) and tumor-associated autoantibodies (AAbs). In this review, we summarize the latest progress of liquid biopsies, and their potential applications and challenges in the diagnosis of malignant pulmonary nodules.

Transplantation of GMP-grade human iPSC-derived retinal pigment epithelial cells in rodent model: the first pre-clinical study for safety and efficacy in China.

BACKGROUND: Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly due in large part to age-dependent atrophy of retinal pigment epithelium (RPE) cells. RPE cells form a monolayer located between the choroid and the outer segments of photoreceptors, playing multifarious roles in maintenance of visual function. Allogeneically induced pluripotent stem cell-derived RPE (iPSC-RPE or iRPE) has become a potential approach for providing an abundant source of donors for clinical cell products. Transplantation of iRPE has been proven effective in rescuing impaired retinas in Royal College of Surgeons (RCS) rats after approximately 5 to 6 weeks. Here, we explore the long-term (19 weeks) safety and efficacy of human iRPE cell transplantation in pre-clinical animal models. METHODS: The expression of human RPE-specific markers in iRPE cells was determined using immunofluorescence staining. For the proliferative test, Ki-67 expression was also verified by immunofluorescence and flow cytometric analysis. Then, iRPE cells were transplanted into the subretinal space of immune-deficient NOD/SCID/IL-2Rgcnull (NSG) mice to assess their safety. To evaluate whether the transplanted cells could survive and rescue visual function, we performed color fundus photography, focal electroretinogram and immunostaining after delivering iRPE cells into the subretinal space of RCS rats. RESULTS: Human iRPE cells expressed native RPE-specific markers, such as microphthalmia-associated transcription factor (MiTF), retinal pigment epithelium-specific 65-kDa protein (RPE65) and tight-junction associated structural protein (ZO-1), and their proliferative capacity (Ki-67 expression) was poor after 25 days of induction. A tumorigenicity test revealed no tumor formation or abnormal proliferation in the immunodeficient mice after subretinal injection of 5×105 iRPE cells. The transplanted iRPE cells survived for at least 19 weeks and maintained visual function for 15 weeks. CONCLUSIONS: In the present study, we provided further evidence for the use of human iRPE transplantation to treat retinal degenerative disease in pre-clinical animal models. Therefore, we consider human iRPE cells a promising source of cell replacement therapy for AMD.

Enrichment and detection of circulating tumor cells by immunomagnetic beads and flow cytometry.

OBJECTIVE: The purpose of the article is to establish a quick enrichment and detection method using immunomagnetic beads and flow cytometry to analyze circulating tumor cells (CTCs) in the peripheral blood. RESULTS: After incubation with CD326-PE and CD45-APC antibodies, more than 60% MCF7 cells in M-Buffer could be detected while less than 10% of the same cells could be detected by flow cytometry (FCM) if spiked into blood. However, in combination with CD326 and CD45 immunomagnetic beads, detection rate of MCF7 cells in blood reached 57%. For circulating tumor cells, enrichment by CD326 and CD45 immunomagnetic beads improve the detection rate from nearly undetectable to more than 24.14%. CONCLUSIONS: Live CTCs in peripheral blood can be effectively and sensitively detected by using a combination of immunomagnetic beads (CD45 and CD326) and flow cytometry.

CpGtools: a python package for DNA methylation analysis.

MOTIVATION: DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. RESULTS: We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) 'CpG position modules' focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) 'CpG signal modules' are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) 'differential CpG analysis modules' focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. AVAILABILITY AND IMPLEMENTATION: CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization and generation of human definitive multipotent hematopoietic stem/progenitor cells.

Definitive hematopoiesis generates hematopoietic stem/progenitor cells (HSPCs) that give rise to all mature blood and immune cells, but remains poorly defined in human. Here, we resolve human hematopoietic populations at the earliest hematopoiesis stage by single-cell RNA-seq. We characterize the distinct molecular profiling between early primitive and definitive hematopoiesis in both human embryonic stem cell (hESC) differentiation and early embryonic development. We identify CD44 to specifically discriminate definitive hematopoiesis and generate definitive HSPCs from hESCs. The multipotency of hESCs-derived HSPCs for various blood and immune cells is validated by single-cell clonal assay. Strikingly, these hESCs-derived HSPCs give rise to blood and lymphoid lineages in vivo. Lastly, we characterize gene-expression dynamics in definitive and primitive hematopoiesis and reveal an unreported role of ROCK-inhibition in enhancing human definitive hematopoiesis. Our study provides a prospect for understanding human early hematopoiesis and a firm basis for generating blood and immune cells for clinical purposes.

Author Correction: Glis1 facilitates induction of pluripotency via an epigenome-metabolome-epigenome signalling cascade.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of an autophagy-related gene signature that can improve prognosis of hepatocellular carcinoma patients.

BACKGROUND: Autophagy is a programmed cell degradation mechanism that has been associated with several physiological and pathophysiological processes, including malignancy. Improper induction of autophagy has been proposed to play a pivotal role in the progression of hepatocellular carcinoma (HCC). METHODS: Univariate Cox regression analysis of overall survival (OS) was performed to identify risk-associated autophagy-related genes (ARGs) in HCC data set from The Cancer Genome Atlas (TCGA). Multivariate cox regression was then performed to develop a risk prediction model for the prognosis of 370 HCC patients. The multi-target receiver operating characteristic (ROC) curve was used to determine the model's accuracy. Besides, the relationship between drug sensitivity and ARGs expression was also examined. RESULTS: A total of 62 differentially expressed ARGs were identified in HCC patients. Univariate and multivariate regression identified five risk-associated ARGs (HDAC1, RHEB, ATIC, SPNS1 and SQSTM1) that were correlated with OS in HCC patients. Of importance, the risk-associated ARGs were independent risk factors in the multivariate risk model including clinical parameters such as malignant stage (HR = 1.433, 95% CI = 1.293-1.589, P < 0.001). In addition, the area under curve for the prognostic risk model was 0.747, which indicates the high accuracy of the model in prediction of HCC outcomes. Interestingly, the risk-associated ARGs were also correlated with drug sensitivity in HCC cell lines. CONCLUSION: We developed a novel prognostic risk model by integrating the molecular signature and clinical parameters of HCC, which can effectively predict the outcomes of HCC patients.

Glis1 facilitates induction of pluripotency via an epigenome-metabolome-epigenome signalling cascade.

Somatic cell reprogramming provides insight into basic principles of cell fate determination, which remain poorly understood. Here we show that the transcription factor Glis1 induces multi-level epigenetic and metabolic remodelling in stem cells that facilitates the induction of pluripotency. We find that Glis1 enables reprogramming of senescent cells into pluripotent cells and improves genome stability. During early phases of reprogramming, Glis1 directly binds to and opens chromatin at glycolytic genes, whereas it closes chromatin at somatic genes to upregulate glycolysis. Subsequently, higher glycolytic flux enhances cellular acetyl-CoA and lactate levels, thereby enhancing acetylation (H3K27Ac) and lactylation (H3K18la) at so-called 'second-wave' and pluripotency gene loci, opening them up to facilitate cellular reprogramming. Our work highlights Glis1 as a powerful reprogramming factor, and reveals an epigenome-metabolome-epigenome signalling cascade that involves the glycolysis-driven coordination of histone acetylation and lactylation in the context of cell fate determination.

AURKA rs2273535 T>A Polymorphism Associated With Cancer Risk: A Systematic Review With Meta-Analysis.

Aurora kinase A (AURKA) is a cell cycle regulatory serine/threonine kinase that promotes cell cycle progression. It plays an important role in regulating the transition from G2 to M phase during mitosis. The association between the AURKA rs2273535 T>A polymorphism and cancer risk has been investigated, but the results remain inconsistent. To get a more accurate conclusion, we conducted a comprehensive meta-analysis of 36 case-control studies, involving 22,884 cancer cases and 30,497 healthy controls. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to determine the association of interest. Pooled analysis indicated that the AURKA rs2273535 T>A polymorphism increased the overall risk of cancer (homozygous: OR = 1.17, 95% CI = 1.04-1.33; recessive: OR = 1.15, 95% CI = 1.05-1.25; allele: OR = 1.07, 95% CI = 1.02-1.13). Stratification analysis by cancer type further showed that this polymorphism was associated with an increased breast cancer risk. This meta-analysis indicated that the AURKA rs2273535 T>A polymorphism was associated with an overall increased cancer risk, especially breast cancer. Further validation experiments are needed to strengthen our conclusion.

PuHox52-mediated hierarchical multilayered gene regulatory network promotes adventitious root formation in Populus ussuriensis.

Adventitious root (AR) formation is critically important in vegetative propagation through cuttings in some plants, especially woody species. However, the underlying molecular mechanisms remain elusive. Here, we report the identification of a poplar homeobox gene, PuHox52, which was induced rapidly (within 15 min) at the basal ends of stems upon cutting and played a key regulatory role in adventitious rooting. We demonstrated that overexpression of PuHox52 significantly increased the number of ARs while suppression of PuHox52 had the opposite effect. A multilayered hierarchical gene regulatory network (ML-hGRN) mediated by PuHox52 was reverse-engineered and demonstrated to govern AR formation. PuHox52 regulated AR formation through upregulation of nine hub regulators, including a jasmonate signaling pathway gene, PuMYC2, and an auxin signaling pathway gene, PuAGL12. We also identified coherent type 4 feed-forward loops within this ML-hGRN; PuHox52 repressed PuHDA9, which encodes a histone deacetylase, and led to an increase in acetylation and presumably expression of three hub regulators, PuWRKY51, PuLBD21 and PuIAA7. Our results indicate that the ML-hGRN mediated by PuHox52 governs AR formation at the basal ends of stem cuttings from poplar trees.