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Pregnancy reprograms the epigenome of mammary epithelial cells and blocks the development of premalignant mammary lesions in response to cMYC-overexpression.
No full textStructural Basis of Functional Transitions in Mammalian NMDA Receptors.
No full textExcitatory neurotransmission meditated by glutamate receptors including N-methyl-D-aspartate receptors (NMDARs) is pivotal to brain development and function. NMDARs are heterotetramers composed of GluN1 and GluN2 subunits, which bind glycine and glutamate, respectively, to activate their ion channels. Despite importance in brain physiology, the precise mechanisms by which activation and inhibition occur via subunit-specific binding of agonists and antagonists remain largely unknown. Here, we show the detailed patterns of conformational changes and inter-subunit and -domain reorientation leading to agonist-gating and subunit-dependent competitive inhibition by providing multiple structures in distinct ligand states at 4 Å or better. The structures reveal that activation and competitive inhibition by both GluN1 and GluN2 antagonists occur by controlling the tension of the linker between the ligand-binding domain and the transmembrane ion channel of the GluN2 subunit. Our results provide detailed mechanistic insights into NMDAR pharmacology, activation, and inhibition, which are fundamental to the brain physiology
CoCoCoNet: Conserved and Comparative Co-expression Across a Diverse Set of Species
Get PDFABSTRACT Co-expression analysis has provided insight into gene function in organisms from Arabidopsis to Zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties, or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying co nserved gene modules and co mparing co -expression net works. CoCoCoNet is a resource for both data and methods, providing gold-standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups, and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet , with data and R scripts available at ftp://milton.cshl.edu/data
Polyethnic-1000: Advancing cancer genomics by studying ethnically diverse, underserved patient populations in New York
No full textRecent advances in DNA sequencing technologies have revolutionized approaches to the prevention, risk assessment, early detection, diagnosis, and treatment of cancers. However, many ethnic groups, especially nonEuropean populations, have been significantly underrepresented in cancer research, including clinical trials, and have not received equal benefits in clinical practice. As a result, our current knowledge about tumor biology, cancer risk, and response to treatment has primarily been derived from patients of European descent. These inequities limit our understanding of the many types of cancer and may exacerbate health disparities in the United States. In this proposal, we address both the scientific and social issues by creating a dynamic research platform within the greater New York area that promises to enhance the ways in which cancer is prevented, diagnosed, and treated. This initiative, named Polyethnic-1000, will involve patients and staff at several academic health centers and partner hospitals in the New York City region. It will use the genomics and informatics capabilities of the New York Genome Center to determine how inherited and somatically acquired genetic variations affect the behavior of cancers occurring in ethnically diverse populations. In a first, retrospective phase, we will establish the workflow from sample acquisition to whole-exome and RNA sequencing, data analysis, and data sharing within the consortium. Then we will start a prospective study enabling the formation of cohorts of interest for particular cancer types and particular ethnicities, with uniform consent allowing broad data sharing of the somatic variants identified. Polyethnic-1000 will establish a framework to enhance interactions among our region's academic and health centers to advance cancer genomics. These efforts should improve and widen the use of genomics for all, especially currently underserved ethnic minority populations
Hallmarks of Splicing Defects in Cancer: Clinical Applications in the Era of Personalized Medicine
Get PDFAlternative splicing promotes proteome diversity by using limited number of genes, a key control point of gene expression. Splicing is carried out by large macromolecular machineries, called spliceosome, composed of small RNAs and proteins. Alternative splicing is regulated by splicing regulatory cis-elements in RNA and trans-acting splicing factors that are often tightly regulated in a tissue-specific and developmental stage-specific manner. The biogenesis of ribonucleoprotein (RNP) complexes is strictly regulated to ensure that correct complements of RNA and proteins are coordinated in the right cell at the right time to support physiological functions. Any perturbations that impair formation of functional spliceosomes by disrupting the cis-elements, or by compromising RNA-binding or function of trans-factors can be deleterious to cells and result in pathological consequences. The recent discovery of oncogenic mutations in splicing factors, and growing evidence of the perturbed splicing in multiple types of cancer, underscores RNA processing defects as a critical driver of oncogenesis. These findings have resulted in a growing interest in targeting RNA splicing as a therapeutic approach for cancer treatment. This review summarizes our current understanding of splicing alterations in cancer, recent therapeutic efforts targeting splicing defects in cancer, and future potentials to develop novel cancer therapies
MALAT1 Long Non-Coding RNA: Functional Implications
Get PDFThe mammalian genome is pervasively transcribed and the functional significance of many long non-coding RNA (lncRNA) transcripts are gradually being elucidated. Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) is one of the most well-studied lncRNAs. MALAT1 is a highly conserved nuclear retained lncRNA that is abundantly expressed in cells and tissues and has been shown to play a role in regulating genes at both the transcriptional and post-transcriptional levels in a context-dependent manner. However, Malat1 has been shown to be dispensable for normal development and viability in mice. Interestingly, accumulating evidence suggests that MALAT1 plays an important role in numerous diseases including cancer. Here, we discuss the current state-of-knowledge in regard to MALAT1 with respect to its function, role in diseases, and the potential therapeutic opportunities for targeting MALAT1 using antisense oligonucleotides and small molecules
Competence against insufficiency: Why are men mostly safe from a rare and deadly prostate cancer?
No full textProstate cancer is a slow-growing disease, but not always. A highly rare and lethal form of the disease shows survival rates of less than a year. It is called squamous cell prostate carcinoma. In this issue of JEM, Hermanova et al. (https://doi.org/10.1084/jem.20191787) provide new findings in mouse demonstrating a strong genetic handle on both the reasons behind the rarity and the aggressiveness
A community-based transcriptomics classification and nomenclature of neocortical cell types
No full textTo understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body
Analytical guidelines for co-fractionation mass spectrometry obtained through global profiling of gold standard Saccharomyces cerevisiae protein complexes
No full textCo-fractionation mass spectrometry (CF-MS) is a technique with potential to characterise endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS datasets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution dataset, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies - Spearman and Kendall correlations - and the recently introduced Co-apex metric frequently maximise recall, while a popular metric - Euclidean distance - delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying non-model organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimise false discovery. These assessments are summarised in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation