RESUMO
N6-methyladenosine (m6A) RNA modification is the most prevalent form of RNA methylation and plays a crucial role in plant development. However, our understanding of m6A modification in Masson pine (Pinus massoniana Lamb.) remains limited. In this study, a complete analysis of m6A writers, erasers, and readers in Masson pine was performed, and 22 m6A regulatory genes were identified in total, including 7 m6A writers, 7 m6A erases, and 8 readers. Phylogenetic analysis revealed that all m6A regulators involved in Masson pine could be classified into three distinct groups based on their domains and motifs. The tissue expression analysis revealed that the m6A regulatory gene may exert a significant influence on the development of reproductive organs and leaves in Masson pine. Moreover, the results from stress and hormone expression analysis indicated that the m6A regulatory gene in Masson pine might be involved in drought stress response, ABA-signaling-pathway activation, as well as resistance to Monochamus alternatus. This study provided valuable and anticipated insights into the regulatory genes of m6A modification and their potential epigenetic regulatory mechanisms in Masson pine.
Assuntos
Adenosina , Regulação da Expressão Gênica de Plantas , Filogenia , Pinus , Estresse Fisiológico , Transcriptoma , Pinus/genética , Pinus/metabolismo , Estresse Fisiológico/genética , Adenosina/análogos & derivados , Adenosina/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Perfilação da Expressão Gênica , Epigênese GenéticaRESUMO
Biotic and abiotic stresses have already seriously restricted the growth and development of Pinus massoniana, thereby influencing the quality and yield of its wood and turpentine. Recent studies have shown that C2H2 zinc finger protein transcription factors play an important role in biotic and abiotic stress response. However, the members and expression patterns of C2H2 TFs in response to stresses in P. massoniana have not been performed. In this paper, 57 C2H2 zinc finger proteins of P. massoniana were identified and divided into five subgroups according to a phylogenetic analysis. In addition, six Q-type PmC2H2-ZFPs containing the plant-specific motif 'QALGGH' were selected for further study under different stresses. The findings demonstrated that PmC2H2-ZFPs exhibit responsiveness towards various abiotic stresses, including drought, NaCl, ABA, PEG, H2O2, etc., as well as biotic stress caused by the pine wood nematode. In addition, PmC2H2-4 and PmC2H2-20 were nuclear localization proteins, and PmC2H2-20 was a transcriptional activator. PmC2H2-20 was selected as a potential transcriptional regulator in response to various stresses in P. massoniana. These findings laid a foundation for further study on the role of PmC2H2-ZFPs in stress tolerance.
Assuntos
Dedos de Zinco CYS2-HIS2 , Regulação da Expressão Gênica de Plantas , Filogenia , Pinus , Proteínas de Plantas , Estresse Fisiológico , Fatores de Transcrição , Pinus/genética , Pinus/parasitologia , Pinus/metabolismo , Estresse Fisiológico/genética , Dedos de Zinco CYS2-HIS2/genética , Fatores de Transcrição/metabolismo , Fatores de Transcrição/genética , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Transcriptoma , Perfilação da Expressão Gênica , Dedos de ZincoRESUMO
BACKGROUND: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE: Retrospective. POPULATION: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. ASSESSMENT: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. STATISTICAL TESTS: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. RESULTS: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively. DATA CONCLUSION: AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.
Assuntos
Aprendizado Profundo , Neoplasias da Próstata , Radiologia , Inteligência Artificial , Humanos , Imageamento por Ressonância Magnética , Masculino , Neoplasias da Próstata/diagnóstico por imagem , Estudos RetrospectivosRESUMO
Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system. Among the enhancers uncovered, we focused on a long-range enhancer â¼664 kb from the ONECUT1 promoter, since coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hPSCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. This enhancer contains a confidently fine-mapped type 2 diabetes associated variant (rs528350911) which disrupts a GATA motif. Introduction of the risk variant into hPSCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a slight reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.
RESUMO
BACKGROUND: Renal cancer is insensitive to radiotherapy or most chemotherapies. While the loss of the XPC gene was correlated with drug resistance in colon cancer, the expression of XPC and its role in the drug resistance of renal cancer have not yet been elucidated. With the fact that natural small-molecules have been adopted in combinational therapy with classical chemotherapeutic agents to increase the drug sensitivity and reduce adverse effects, the use of herbal compounds to tackle drug-resistance in renal cancer is advocated. PURPOSE: To correlate the role of XPC gene deficiency to drug-resistance in renal cancer, and to identify natural small-molecules that can reverse drug-resistance in renal cancer via up-regulation of XPC. METHODS: IHC was adopted to analyze the XPC expression in human tumor and adjacent tissues. Clinical data extracted from The Cancer Genome Atlas (TCGA) database were further analysed to determine the relationship between XPC gene expression and tumor staging of renal cancer. Two types of XPC-KD renal cancer cell models were established to investigate the drug-resistant phenotype and screen XPC gene enhancers from 134 natural small-molecules derived from herbal plants. Furthermore, the identified XPC enhancers were verified in single or in combination with FDA-approved chemotherapy drugs for reversing drug-resistance in renal cancer using MTT cytotoxicity assay. Drug resistance gene profiling, ROS detection assay, immunocytochemistry and cell live-dead imaging assay were adopted to characterize the XPC-related drug resistant mechanism. RESULTS: XPC gene expression was significantly reduced in renal cancer tissue compared with its adjacent tissue. Clinical analysis of TCGA database also identified the downregulated level of XPC gene in renal tumor tissue of stage IV patients with cancer metastasis, which was also correlated with their lower survival rate. 6 natural small-molecules derived from herbal plants including tectorigenin, pinostilbene, d-pinitol, polygalasaponin F, atractylenolide III and astragaloside II significantly enhanced XPC expression in two renal cancer cell types. Combinational treatment of the identified natural compound with the treatment of FDA-approved drug, further confirmed the up-regulation of XPC gene expression can sensitize the two types of XPC-KD drug-resistant renal cancer cells towards the FDA-approved drugs. Mechanistic study confirmed that GSTP1/ROS axis was activated in drug resistant XPC-KD renal cancer cells. CONCLUSION: XPC gene deficiency was identified in patient renal tumor samples, and knockdown of the XPC gene was correlated with a drug-resistant phenotype in renal cancer cells via activation of the GSTP1/ROS axis. The 6 identified natural small molecules were confirmed to have drug sensitizing effects via upregulation of the XPC gene. Therefore, the identified active natural small molecules may work as an adjuvant therapy for circumventing the drug-resistant phenotype in renal cancer via enhancement of XPC expression.
Assuntos
Carcinoma de Células Renais , Neoplasias Renais , Xeroderma Pigmentoso , Humanos , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Carcinoma de Células Renais/tratamento farmacológico , Carcinoma de Células Renais/genética , Espécies Reativas de Oxigênio , Neoplasias Renais/tratamento farmacológico , Neoplasias Renais/genética , Resistência a MedicamentosRESUMO
BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING: US National Institutes of Health.