Histone Lysine Methyltransferase SDG8 Is Involved in Brassinosteroid-Regulated Gene Expression in Arabidopsis thaliana

Citation: Wang, X., Chen, J., Xie, Z., Liu, S., Nolan, T., Ye, H., et al. (2014). Histone lysine methyltransferase SDG8 is involved in brassinosteroid- regulated gene expression in arabidopsis thaliana.The plant steroid hormones, brassinosteroids (BRs), play important roles in plant growth, development and responses to environmental stresses. BRs signal through receptors localized to the plasma membrane and other signaling components to regulate the BES1/BZR1 family of transcription factors, which modulates the expression of 4,000-5,000 genes. How BES1/BZR1 and their interacting proteins function to regulate the large number of genes are not completely understood. Here we report that histone lysine methyltransferase SDG8, implicated in Histone 3 lysine 36 di- and tri-methylation (H3K36me2 and me3), is involved in BR-regulated gene expression. BES1 interacts with SDG8, directly or indirectly through IWS1, a transcription elongation factor involved in BR-regulated gene expression. The knockout mutant sdg8 displays a reduced growth phenotype with compromised BR responses. Global gene expression studies demonstrated that SDG8 plays a major role in BR-regulated gene expression as more than half of BR-regulated genes are differentially affected in sdg8 mutant. A Chromatin Immunoprecipitation (ChIP) experiment showed that H3K36me3 is reduced in BR-regulated genes in the sdg8 mutant. Based on these results, we propose that SDG8 plays an essential role in mediating BR-regulated gene expression. Our results thus reveal a major mechanism by which histone modifications dictate hormonal regulation of gene expression

Selective Autophagy of BES1 Mediated by DSK2 Balances Plant Growth and Survival

Plants encounter a variety of stresses and must fine-tune their growth and stress-response programs to best suit their environment. BES1 functions as a master regulator in the brassinosteroid (BR) pathway that promotes plant growth. Here, we show that BES1 interacts with the ubiquitin receptor protein DSK2 and is targeted to the autophagy pathway during stress via the interaction of DSK2 with ATG8, a ubiquitin-like protein directing autophagosome formation and cargo recruitment. Additionally, DSK2 is phosphorylated by the GSK3-like kinase BIN2, a negative regulator in the BR pathway. BIN2 phosphorylation of DSK2 flanking its ATG8 interacting motifs (AIMs) promotes DSK2-ATG8 interaction, thereby targeting BES1 for degradation. Accordingly, loss-of-function dsk2 mutants accumulate BES1, have altered global gene expression profiles, and have compromised stress responses. Our results thus reveal that plants coordinate growth and stress responses by integrating BR and autophagy pathways and identify the molecular basis of this crosstalk

Case Report: A rare synchronous multiple gastric carcinoma achieved progression-free disease through NGS-guided serial treatment

Synchronous multiple gastric carcinoma (SMGC) is a rare condition characterized by the simultaneous occurrence of two or more primary malignant tumors in the stomach, each with its own distinct pathological morphology. SMGC differs from gastric metastases, which originate from primary gastric or non-gastric tumors. At present, the incidence of SMGC is low in China, with no established guidelines for standard treatment. Here, we report a rare case of advanced SMGC that achieved long-lasting clinical benefits through a treatment strategy informed by next-generation sequencing (NGS). Dynamically monitoring of the tumor and/or circulating cell-free DNA guided the patient’s treatment sequentially. The patient received anti-HER2 therapy, followed by immunotherapy, pembrolizumab in combination with trastuzumab and chemotherapy, and ultimately underwent successful total gastrectomy. This case highlights a novel approach of utilizing liquid biopsy-based NGS to gain insights into disease progression and molecular response to NGS-guided treatment in SMGC patients

Case Report: Cancer spectrum and genetic characteristics of a de novo germline POLD1 p.L606M variant-induced polyposis syndrome

Germline variations in the DNA polymerase genes, POLE and POLD1, can lead to a hereditary cancer syndrome that is characterized by frequent gastrointestinal polyposis and multiple primary malignant tumors. However, because of its rare occurrence, this disorder has not been extensively studied. In this report, we present the case of a 22-year-old female patient who had been diagnosed with gastrointestinal polyposis, breast fibroadenoma, multiple primary colorectal cancers, and glioblastoma (grade IV) within a span of 4 years. Next-generation sequencing analysis revealed a germline variant in POLD1 (c.1816C>A; p.L606M). In silico analysis using protein functional predicting software, including SIFT, Polyphen, GERP++, and CADD, further confirmed the pathogenicity of POLD1 p.L606M (classified as ACMG grade Class 4). In line with polymerase deficiency, both rectal cancer and glioblastoma tissues exhibited a high tumor mutation burden, with 16.9 muts/Mb and 347.1 muts/Mb, respectively. Interestingly, the patient has no family history of cancer, and gene examination of both parents confirms that this is a de novo germline variant. Therefore, molecular screening for POLD1 may be necessary for patients with such a cancer spectrum, regardless of their family history

Clinical validity assessment of genes frequently tested on intellectual disability/autism sequencing panels.

[en] PURPOSE: Neurodevelopmental disorders (NDDs), such as intellectual disability (ID) and autism spectrum disorder (ASD), exhibit genetic and phenotypic heterogeneity, making them difficult to differentiate without a molecular diagnosis. The Clinical Genome Resource Intellectual Disability/Autism Gene Curation Expert Panel (GCEP) uses systematic curation to distinguish ID/ASD genes that are appropriate for clinical testing (ie, with substantial evidence supporting their relationship to disease) from those that are not. METHODS: Using the Clinical Genome Resource gene-disease validity curation framework, the ID/Autism GCEP classified genes frequently included on clinical ID/ASD testing panels as Definitive, Strong, Moderate, Limited, Disputed, Refuted, or No Known Disease Relationship. RESULTS: As of September 2021, 156 gene-disease pairs have been evaluated. Although most (75%) were determined to have definitive roles in NDDs, 22 (14%) genes evaluated had either Limited or Disputed evidence. Such genes are currently not recommended for use in clinical testing owing to the limited ability to assess the effect of identified variants. CONCLUSION: Our understanding of gene-disease relationships evolves over time; new relationships are discovered and previously-held conclusions may be questioned. Without periodic re-examination, inaccurate gene-disease claims may be perpetuated. The ID/Autism GCEP will continue to evaluate these claims to improve diagnosis and clinical care for NDDs

CELLULAR AND MOLECULAR PATHOGENESIS OF LEFT VENTRICULAR HYPERTROPHY IN RAF1- MUTANT NOONAN SYNDROME

Noonan syndrome (NS) belongs to a class of autosomal dominant disorders, termed “RASopathies”, caused by mutations in RAS/ERK pathway genes and characterized by craniofacial, growth, cognitive and cardiac defects. NS patients with kinase-activating RAF1 alleles typically develop pathological left ventricular hypertrophy (LVH), which is reproduced in Raf1L613V/+ knock-in mice. Here, using inducible Raf1L613V expression, I show that LVH results from the interplay of cardiac cell types. Cardiomyocyte expression of Raf1L613V enhances Ca2+ sensitivity and cardiac contractility without causing hypertrophy. Raf1L613V expression in cardiomyocytes or activated fibroblasts exacerbates pressure overload-evoked fibrosis. Endothelial/endocardial (EC) Raf1L613V expression causes cardiac hypertrophy without affecting contractility. Co-culture and neutralizing antibody experiments reveal a cytokine hierarchy (TNF->IL6) from Raf1L613V-expressing ECs that drives cardiomyocyte hypertrophy in vitro. Furthermore, post-natal TNF inhibition normalizes the increased wall thickness and cardiomyocyte hypertrophy in vivo. I conclude that NS cardiomyopathy involves cardiomyocytes, ECs, and fibroblasts, TNF/IL6 signaling components represent potential therapeutic targets, and abnormal EC signaling might contribute to other forms of LVH. On the other hand, little is known about the relative contribution of ERK1 and ERK2 to the pathogenesis of LVH. Using genetic ablation of Erk1 or Erk2, globally or in a tissue-specific manner, I show that while ERK2 is the predominant ERK isoform in the heart and isolated cardiomyocytes, ablation of Erk1, but not Erk2, normalizes cardiac hyper-contractility. ERK1 and, to a greater extent, ERK2 mediates ventricular chamber size. While normalization of the increased heart mass is not observed with progressive decrease in Erk1/2 gene dosage, it is achieved with endothelial-specific knockout of Erk2. Notably, ERK1 is the major ERK isoform in cardiac endothelial cells. Taken together, these data suggest isoform-specific roles of ERK1 and ERK2 in the pathogenesis of RAF1 mutant-associated LVH.Ph.D.2019-11-19 00:00:0

Bayesian Nonparametric Models for Multi-Stage Sample Surveys

It is a standard practice in small area estimation (SAE) to use a model-based approach to borrow information from neighboring areas or from areas with similar characteristics. However, survey data tend to have gaps, ties and outliers, and parametric models may be problematic because statistical inference is sensitive to parametric assumptions. We propose nonparametric hierarchical Bayesian models for multi-stage finite population sampling to robustify the inference and allow for heterogeneity, outliers, skewness, etc. Bayesian predictive inference for SAE is studied by embedding a parametric model in a nonparametric model. The Dirichlet process (DP) has attractive properties such as clustering that permits borrowing information. We exemplify by considering in detail two-stage and three-stage hierarchical Bayesian models with DPs at various stages. The computational difficulties of the predictive inference when the population size is much larger than the sample size can be overcome by the stick-breaking algorithm and approximate methods. Moreover, the model comparison is conducted by computing log pseudo marginal likelihood and Bayes factors. We illustrate the methodology using body mass index (BMI) data from the National Health and Nutrition Examination Survey and simulated data. We conclude that a nonparametric model should be used unless there is a strong belief in the specific parametric form of a model

A Bayesian Small Area Model with Dirichlet Processes on the Responses

Typically survey data have responses with gaps, outliers and ties, and the distributions of the responses might be skewed. Usually, in small area estimation, predictive inference is done using a two-stage Bayesian model with normality at both levels (responses and area means). This is the Scott-Smith (S-S) model and it may not be robust against these features. Another model that can be used to provide a more robust structure is the two-stage Dirichlet process mixture (DPM) model, which has independent normal distributions on the responses and a single Dirichlet process on the area means. However, this model does not accommodate gaps, outliers and ties in the survey data directly. Because this DPM model has a normal distribution on the responses, it is unlikely to be realized in practice, and this is the problem we tackle in this paper. Therefore, we propose a two-stage non-parametric Bayesian model with several independent Dirichlet processes at the first stage that represents the data, thereby accommodating some of the difficulties with survey data and permitting a more robust predictive inference. This model has a Gaussian (normal) distribution on the area means, and so we call it the DPG model. Therefore, the DPM model and the DPG model are essentially the opposite of each other and they are both different from the S-S model. Among the three models, the DPG model gives us the best head-start to accommodate the features of the survey data. For Bayesian predictive inference, we need to integrate two data sets, one with the responses and other with area sizes. An application on body mass index, which is integrated with census data, and a simulation study are used to compare the three models (S-S, DPM, DPG); we show that the DPG model might be preferred