Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma.

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

Integrated Bioinformatics and Experimental Validation to Identify a Disulfidptosis-Related lncRNA Model for Prognostic Prediction in Papillary Renal Cell Carcinoma.

AIMS: This study aimed to construct a prognostic model for papillary renal cell carcinoma (pRCC) utilizing disulfidptosis-associated long non-coding RNAs (lncRNAs). Additionally, it investigated the potential of these lncRNAs in predicting immune responses and drug sensitivity in pRCC. BACKGROUND: LncRNAs have been implicated in the progression and prognosis of pRCC. Recently, disulfidptosis, an emerging form of regulated cell death, has shown potential as a therapeutic approach for cancer. However, the potential association between disulfidptosis-related lncRNAs and pRCC remains unclear. METHODS: We analyzed transcriptome profiling and clinical data of pRCC patients from The Cancer Genome Atlas database. Using Pearson correlation analysis, we identified lncRNAs associated with disulfidptosis. Based on the identified disulfidptosis-related lncRNAs that were correlated with overall survival (OS), we constructed a novel prediction model using the least absolute shrinkage and selection operator, univariable Cox regression, and multivariable Cox regression analyses. The model's utility was assessed through Kaplan-Meier survival, receiver operating characteristics, and principal component analyses. Moreover, functional analysis helped identify potential prognostic mechanisms, and the prediction of chemical drugs for pRCC was also performed. Finally, qRT-PCR validated the expression of prognostic lncRNAs in pRCC cells and patient samples. RESULTS: Our prediction model was based on nine disulfidptosis-related lncRNAs. Evaluation and validation analyses demonstrated that the model had excellent, consistent, and independent prognostic value for pRCC patients, with area under the curve (AUC) values of 0.954, 0.910, and 0.830 for 1-, 3-, and 5-year OS, respectively. Through functional analysis, we discovered a significant correlation between the identified prognostic signature and immunity. Additionally, in terms of chemotherapy sensitivity, our analysis indicated that the low-risk group exhibited higher sensitivity to sunitinib and pazopanib. Furthermore, the expression patterns of the identified lncRNAs were validated in samples obtained from pRCC cells and patients. CONCLUSION: This study successfully established and validated a novel disulfidptosis-related prediction model. The findings suggest the potential involvement of immune-related pathways in lncRNA signature-associated survival. This model holds promise for differentiating prognosis and improving personalized therapeutic strategies for pRCC in clinical practice.

Construction and experimental validation of a novel ferroptosis-related gene signature for myelodysplastic syndromes.

BACKGROUND: Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by morphological abnormalities and peripheral blood cytopenias, carrying a risk of progression to acute myeloid leukemia. Although ferroptosis is a promising target for MDS treatment, the specific roles of ferroptosis-related genes (FRGs) in MDS diagnosis have not been elucidated. METHODS: MDS-related microarray data were obtained from the Gene Expression Omnibus database. A comprehensive analysis of FRG expression levels in patients with MDS and controls was conducted, followed by the use of multiple machine learning methods to establish prediction models. The predictive ability of the optimal model was evaluated using nomogram analysis and an external data set. Functional analysis was applied to explore the underlying mechanisms. The mRNA levels of the model genes were verified in MDS clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: The extreme gradient boosting model demonstrated the best performance, leading to the identification of a panel of six signature genes: SREBF1, PTPN6, PARP9, MAP3K11, MDM4, and EZH2. Receiver operating characteristic curves indicated that the model exhibited high accuracy in predicting MDS diagnosis, with area under the curve values of 0.989 and 0.962 for the training and validation cohorts, respectively. Functional analysis revealed significant associations between these genes and the infiltrating immune cells. The expression levels of these genes were successfully verified in MDS clinical samples. CONCLUSION: Our study is the first to identify a novel model using FRGs to predict the risk of developing MDS. FRGs may be implicated in MDS pathogenesis through immune-related pathways. These findings highlight the intricate correlation between ferroptosis and MDS, offering insights that may aid in identifying potential therapeutic targets for this debilitating disorder.

An ARF gene mutation creates flint kernel architecture in dent maize.

Dent and flint kernel architectures are important characteristics that affect the physical properties of maize kernels and their grain end uses. The genes controlling these traits are unknown, so it is difficult to combine the advantageous kernel traits of both. We found mutation of ARFTF17 in a dent genetic background reduces IAA content in the seed pericarp, creating a flint-like kernel phenotype. ARFTF17 is highly expressed in the pericarp and encodes a protein that interacts with and inhibits MYB40, a transcription factor with the dual functions of repressing PIN1 expression and transactivating genes for flavonoid biosynthesis. Enhanced flavonoid biosynthesis could reduce the metabolic flux responsible for auxin biosynthesis. The decreased IAA content of the dent pericarp appears to reduce cell division and expansion, creating a shorter, denser kernel. Introgression of the ARFTF17 mutation into dent inbreds and hybrids improved their kernel texture, integrity, and desiccation, without affecting yield.

Benzylisoquinoline alkaloids with their antitumor activity from the aerial parts of Corydalis impatiens (pall.) Fisch.

Phytochemical investigation on the aerial parts of Corydalis impatiens (pall.) Fisch (Papaveraceae) resulted in the identification of four previous undescribed benzylisoquinoline alkaloids, impatienines A-D (1-4), together with 14 known analogues (5-18). The structures of these compounds were elucidated by extensive spectroscopic analysis (IR, HR-ESIMS, 1D- and 2D-NMR) as well as ECD calculations. All the compounds obtained were investigated for their inhibitory effect on the growth of A549, H1299 and HepG2 cancer cells. Compounds 7 and 15 exhibited pronounced inhibition against the A549 cancer cells with IC50 values of 6.81 µM and 3.17 µM, while the positive control cisplatin was 1.83 µM. Compounds 1-3 showed moderate inhibitory on the H1299 cancer cells. Compounds 4, 10-12, and 16 showed signiffcant activity against HepG2 cancer cells with IC50 values range of 4.41-8.75 µM.

Nitrogenous chemical constituents and their antitumor activities evaluation in vitro from the aerial parts of Corydalis impatiens (pall.) Fisch.

Four new compounds, impatienines E-H (1-4), together with 18 known ones (R)-N-methylcoclaurine (5), impatienine I (6), thalifoline (7), iseluxine (8), pisoquinoline (9), corydaldine (10), northalifoline (11), noroxyhydrastinine (12), 6,7-methylenedioxy-1(2H)-isoquinolinone (13), N-methylcorydaldine (14), oxyhydrastinine (15), corypalline (16), N-trans-feruloylmethoxytyramine (17), N-trans-feruloyldopamine (18), N-trans-feruloyltyramine (19), N-trans-sinapoyltyramine (20), N-cis-feruloyltyramine (21), N-cis-sinapoyltyramine (22) were obtained from the aerial parts of Corydalis impatiens (pall.) Fisch. Their structures were elucidated by extensive spectroscopic analysis (1D- and 2D-NMR, HR-ESIMS, IR, UV) and/or comparison with reported literature. The inhibitory effects of these isolates were also evaluated against the growth of cancer cells (A549, H1299 and HepG2). Compounds 2 and 4 showed significant inhibitory effect on HepG2 cancer cells with IC50 values of 8.62, 8.32 µM, respectively (positive control cisplatin: IC50, 6.32 µM). Compounds 22 and 4 exhibited moderate inhibitory effects against A549 cancer cells, and the IC50 values were 7.78 and 12.54 µM, respectively (positive control cisplatin: IC50, 1.83 µM).

Cistadesertosides B-E, Four New Diastereomeric Lignan Glycosides from the Stems of Cultural Cistanche deserticola in Tarim Desert.

Four previously undescribed diastereomeric lignan glycosides, namely cistadesertosides B-E (1-4) were isolated from the stems of cultural Cistanche deserticola in Tarim desert. The structures of these compounds were elucidated on the basis of extensive spectroscopic analyses, including IR, HR-ESI-MS, 1D and 2D NMR, circular dichroism (CD) data and chemical degradation. The in vitro anti-inflammatory activity of the isolates was also investigated. It showed that compounds 3 and 4 exhibited potential effects with IC50 values of 21.17 µM and 26.97 µM, respectively (positive control quercetin, IC50 , 10.01 µM).

Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis.

BACKGROUND: Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis. METHODS: Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in MM cell samples. RESULTS: The optimal gene signature was identified using LASSO and SVM-RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature-based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune-related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings. CONCLUSION: We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune-related pathways.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown. METHODS: Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets. RESULTS: Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD. CONCLUSION: We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

A novel highly selective allosteric inhibitor of tyrosine kinase 2 (TYK2) can block inflammation- and autoimmune-related pathways.

BACKGROUND: As a member of the Janus kinase (JAK) family, which includes JAK1, JAK2 and JAK3, tyrosine kinase 2 (TYK2) plays an important role in signal transduction and immune system regulation. Moreover, it is also involved in the development of many types of inflammatory and autoimmune diseases, such as psoriasis and systemic lupus erythematosus (SLE). TYK2 is an attractive therapeutic target, and selective inhibition of TYK2 over other JAK family members is critical for the development of TYK2 small molecule inhibitors. However, targeting the catalytic region of the TYK2 ATP-binding site is a major challenge due to the high structural homology between the catalytic regions of the JAK family proteins. RESULTS: In this study, we developed a novel small molecule inhibitor (QL-1200186) by targeting the pseudokinase regulatory domain (Janus homology 2, JH2) of the TYK2 protein. The binding sites of QL-1200186 were predicted and screened by molecular docking. The inhibitory effects on IFNα, IL-12 and IL-23 signaling were tested in cell lines, human peripheral blood cells and human whole blood. The pharmacokinetic (PK) and pharmacodynamic properties of QL-1200186 were verified in mice. QL-1200186 showed high affinity for TYK2 JH2 and had no apparent selectivity for the TYK2 and JAK homologous kinase domains; these effects were demonstrated using biochemical binding, signaling pathway transduction (JAK1/2/3) and off-target effect assays. More importantly, we revealed that QL-1200186 was functionally comparable and selectivity superior to two clinical-stage TYK2 inhibitors (BMS-986165 and NDI-034858) in vitro. In the PK studies, QL-1200186 exhibited excellent exposure, high bioavailability and low clearance rates in mice. Oral administration of QL-1200186 dose-dependently inhibited interferon-γ (IFNγ) production after interleukin-12 (IL-12) challenge and significantly ameliorated skin lesions in psoriatic mice. CONCLUSION: These findings suggest that QL-1200186 is a highly selective and potent inhibitor of TYK2. QL-1200186 could be an appealing clinical drug candidate for the treatment of psoriasis and other autoimmune diseases. Video Abstract.

Machine learning algorithms identify target genes and the molecular mechanism of matrine against diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for refractory or relapsed DLBCL. OBJECTIVE: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. METHODS: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. RESULTS: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms, respectively. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. CONCLUSION: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

WITHDRAWN: Konjac Glucomannan, in Combination with Cisplatin, Suppresses Lymphoma Malignant Progression by Inducing Ferroptosis

The article has been withdrawn at the request of the authors.Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.The Bentham editorial policy on article withdrawal can be found at https://benthamscience.com/editorialpolicies-main.php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Advances in Drug Discovery Targeting Lysosomal Membrane Proteins.

Lysosomes are essential organelles of eukaryotic cells and are responsible for various cellular functions, including endocytic degradation, extracellular secretion, and signal transduction. There are dozens of proteins localized to the lysosomal membrane that control the transport of ions and substances across the membrane and are integral to lysosomal function. Mutations or aberrant expression of these proteins trigger a variety of disorders, making them attractive targets for drug development for lysosomal disorder-related diseases. However, breakthroughs in R&D still await a deeper understanding of the underlying mechanisms and processes of how abnormalities in these membrane proteins induce related diseases. In this article, we summarize the current progress, challenges, and prospects for developing therapeutics targeting lysosomal membrane proteins for the treatment of lysosomal-associated diseases.

Lysine 2-hydroxyisobutyrylation of NAT10 promotes cancer metastasis in an ac4C-dependent manner.

Posttranslational modifications add tremendous complexity to proteomes; however, gaps remain in knowledge regarding the function and regulatory mechanism of newly discovered lysine acylation modifications. Here, we compared a panel of non-histone lysine acylation patterns in metastasis models and clinical samples, and focused on 2-hydroxyisobutyrylation (Khib) due to its significant upregulation in cancer metastases. By the integration of systemic Khib proteome profiling in 20 paired primary esophageal tumor and metastatic tumor tissues with CRISPR/Cas9 functional screening, we identified N-acetyltransferase 10 (NAT10) as a substrate for Khib modification. We further showed that Khib modification at lysine 823 in NAT10 functionally contribute to metastasis. Mechanistically, NAT10 Khib modification enhances its interaction with deubiquitinase USP39, resulting in increased NAT10 protein stability. NAT10 in turn promotes metastasis by increasing NOTCH3 mRNA stability in an N4-acetylcytidine-dependent manner. Furthermore, we discovered a lead compound #7586-3507 that inhibited NAT10 Khib modification and showed efficacy in tumor models in vivo at a low concentration. Together, our findings bridge newly identified lysine acylation modifications with RNA modifications, thus providing novel insights into epigenetic regulation in human cancer. We propose that pharmacological inhibition of NAT10 K823 Khib modification constitutes a potential anti-metastasis strategy.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Blockade of NMT1 enzymatic activity inhibits N-myristoylation of VILIP3 protein and suppresses liver cancer progression.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Identification of the underlying mechanism of HCC progression and exploration of new therapeutic drugs are urgently needed. Here, a compound library consisting of 419 FDA-approved drugs was taken to screen potential anticancer drugs. A series of functional assays showed that desloratadine, an antiallergic drug, can repress proliferation in HCC cell lines, cell-derived xenograft (CDX), patient-derived organoid (PDO) and patient-derived xenograft (PDX) models. N-myristoyl transferase 1 (NMT1) was identified as a target protein of desloratadine by drug affinity responsive target stability (DARTS) and surface plasmon resonance (SPR) assays. Upregulation of NMT1 expression enhanced but NMT1 knockdown suppressed tumor growth in vitro and in vivo. Metabolic labeling and mass spectrometry analyses revealed that Visinin-like protein 3 (VILIP3) was a new substrate of NMT1 in protein N-myristoylation modification, and high NMT1 or VILIP3 expression was associated with advanced stages and poor survival in HCC. Mechanistically, desloratadine binds to Asn-246 in NMT1 and inhibits its enzymatic activity, disrupting the NMT1-mediated myristoylation of the VILIP3 protein and subsequent NFκB/Bcl-2 signaling. Conclusively, this study demonstrates that desloratadine may be a novel anticancer drug and that NMT1-mediated myristoylation contributes to HCC progression and is a potential biomarker and therapeutic target in HCC.

Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms.

Background: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults. Thus, novel reliable biomarkers need to be further explored to increase diagnostic, therapeutic, and prognostic effectiveness. Methods: Six datasets containing CLL and control samples were downloaded from the Gene Expression Omnibus database. Differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), and the least absolute shrinkage and selection operator (LASSO) regression were applied to identify potential diagnostic biomarkers for CLL using R software. The diagnostic performance of the hub genes was then measured by the receiver operating characteristic (ROC) curve analysis. Functional analysis was implemented to uncover the underlying mechanisms. Additionally, correlation analysis was performed to assess the relationship between the hub genes and immunity characteristics. Results: A total number of 47 differentially expressed genes (DEGs) and 25 candidate hub genes were extracted through differential gene expression analysis and WGCNA, respectively. Based on the 14 overlapped genes between the DEGs and the candidate hub genes, LASSO regression analysis was used, which identified a final number of six hub genes as potential biomarkers for CLL: ABCA6, CCDC88A, PMEPA1, EBF1, FILIP1L, and TEAD2. The ROC curves of the six genes showed reliable predictive ability in the training and validation cohorts, with all area under the curve (AUC) values over 0.80. Functional analysis revealed an abnormal immune status in the CLL patients. A significant correlation was found between the hub genes and the immune-related pathways, indicating a possible tight connection between the hub genes and tumor immunity in CLL. Conclusion: This study was based on machine learning algorithms, and we identified six genes that could be possible CLL markers, which may be involved in CLL pathogenesis and progression through immune-related signal pathways.

THP9 enhances seed protein content and nitrogen-use efficiency in maize.

Teosinte, the wild ancestor of maize (Zea mays subsp. mays), has three times the seed protein content of most modern inbreds and hybrids, but the mechanisms that are responsible for this trait are unknown1,2. Here we use trio binning to create a contiguous haplotype DNA sequence of a teosinte (Zea mays subsp. parviglumis) and, through map-based cloning, identify a major high-protein quantitative trait locus, TEOSINTE HIGH PROTEIN 9 (THP9), on chromosome 9. THP9 encodes an asparagine synthetase 4 enzyme that is highly expressed in teosinte, but not in the B73 inbred, in which a deletion in the tenth intron of THP9-B73 causes incorrect splicing of THP9-B73 transcripts. Transgenic expression of THP9-teosinte in B73 significantly increased the seed protein content. Introgression of THP9-teosinte into modern maize inbreds and hybrids greatly enhanced the accumulation of free amino acids, especially asparagine, throughout the plant, and increased seed protein content without affecting yield. THP9-teosinte seems to increase nitrogen-use efficiency, which is important for promoting a high yield under low-nitrogen conditions.

Targeting PFKL with penfluridol inhibits glycolysis and suppresses esophageal cancer tumorigenesis in an AMPK/FOXO3a/BIM-dependent manner.

As one of the hallmarks of cancer, metabolic reprogramming leads to cancer progression, and targeting glycolytic enzymes could be useful strategies for cancer therapy. By screening a small molecule library consisting of 1320 FDA-approved drugs, we found that penfluridol, an antipsychotic drug used to treat schizophrenia, could inhibit glycolysis and induce apoptosis in esophageal squamous cell carcinoma (ESCC). Gene profiling and Ingenuity Pathway Analysis suggested the important role of AMPK in action mechanism of penfluridol. By using drug affinity responsive target stability (DARTS) technology and proteomics, we identified phosphofructokinase, liver type (PFKL), a key enzyme in glycolysis, as a direct target of penfluridol. Penfluridol could not exhibit its anticancer property in PFKL-deficient cancer cells, illustrating that PFKL is essential for the bioactivity of penfluridol. High PFKL expression is correlated with advanced stages and poor survival of ESCC patients, and silencing of PFKL significantly suppressed tumor growth. Mechanistically, direct binding of penfluridol and PFKL inhibits glucose consumption, lactate and ATP production, leads to nuclear translocation of FOXO3a and subsequent transcriptional activation of BIM in an AMPK-dependent manner. Taken together, PFKL is a potential prognostic biomarker and therapeutic target in ESCC, and penfluridol may be a new therapeutic option for management of this lethal disease.