Biology subjects
Publications and source records attributed to Morris, J. S..
Ephedra sinica is a high-value medicinal plant that produces important phenylpropylamino alkaloids pseudoephedrine and ephedrine. Few genomics resources exist for E. sinica, which has been characterized as a tetraploid with a monoploid genome size of 8.56 Gb. Here we reported a partial genome assembly of E. sinica (12.8 Gb) based on Illumina short-read sequencing at low coverage.
The extent and function of chromatin state aberrations during colorectal cancer (CRC) progression is not completely understood. Here, by comprehensive epigenomic characterization of 56 tumors, adenomas, and their matched normal tissues, we define the dynamics of chromatin states during the progression of colorectal cancer. H3K27ac-marked active enhancer state could distinguish between different stages of CRC progression. By epigenomic editing, we present evidence that gains of tumor-specific enhancers for crucial oncogenes, such as ASCL2 and FZD10, was crucial for excessive proliferation. Consistently, combination of MEK plus bromodomain (BET) inhibition was found to have synergistic effects in CRC patient-derived xenograft (PDX) models. Probing inter-tumor heterogeneity, we identified four distinct enhancer subtypes (EpiC), three of which correlate well with previously defined transcriptomic subtypes (CMSs). Importantly, CMS2 can be divided into two EpiC subgroups with significant survival differences. Leveraging such correlation, we devised a combinatorial therapeutic strategy of enhancer-blocking bromodomain inhibitors with pathway-specific inhibitors (PARPi, EGFRi, and TGF{beta}i) for three EPIC groups. Our data suggest that the dynamics of active enhancer underlies colorectal cancer progression and the patient-specific active enhancer patterns govern their unique gene expression patterns which can be leveraged for precision combination therapy.
The partial reinforcement extinction effect (PREE) is a paradoxical learning phenomenon in which omission of reinforcement during acquisition results in more persistent conditioned responding in extinction. Here, we report a significant PREE with an inverted-U, entropy-like distribution against reinforcement probability following tone foot shock fear conditioning in rats, which was associated with increased neural activity in hippocampus and amygdala as indexed by p-ERK and c-fos immunolabelling. In vivo electrophysiological recordings of local field potentials (LFPs) showed that 50% reinforcement was associated with increases in the frequency and power of tone-evoked theta oscillations in both the subiculum region of hippocampus and in basolateral amygdala (BLA) during both acquisition (Day 1) and extinction (Day 2) sessions. Tone-evoked LFPs in 50% reinforced animals also showed increases in coherence and bidirectional Granger Causality between hippocampus and amygdala. The results support a Bayesian interpretation of the PREE, in which the phenomenon is driven by increases in the entropy or uncertainty of stimulus contingencies, and indicate a crucial role for hippocampus in mediating this uncertainty-dependent effect.
DNA methylation is a key epigenetic factor regulating gene expression. While promoter-associated methylation has been extensively studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to construct gene-level methylation summaries that account for the potentially complex relationships between methylation and expression. We introduce a novel sequential penalized regression approach to construct gene-specific methylation profiles (GSMPs) which find for each gene and tissue type a sparse set of CpGs best explaining gene expression and weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than standard approaches and produces gene-level scores showing key methylation differences across recently discovered colorectal cancer subtypes. We share an R Shiny app that presents GSMP results for colorectal, breast, and pancreatic cancer with plans to extend it to all TCGA cancer types. Our approach yields tissue-specific, gene-specific sparse lists of functionally important CpGs that can be used to construct gene-level methylation scores that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation.
Colorectal carcinogenesis (CRC) progression requires additional molecular mechanisms to APC mutations/aberrant {beta}-catenin signaling. PPARD is a druggable ligand-activated nuclear receptor that regulates essential genes involved in cell fate. PPARD is upregulated in intestinal epithelial cells (IECs) of human colorectal adenomas and adenocarcinomas. The mechanistic significance of PPARD upregulation in CRC remains unknown. Here we show that targeted PPARD overexpression in IECs of mice strongly augmented {beta}-catenin activation via BMP7/TAK1 signaling, promoted intestinal tumorigenesis in Apcmin mice, and accelerated CRC progression and invasiveness in mice with IEC-targeted Apc{Delta}580 mutation. Human CRC invasive fronts had higher PPARD expression than their paired adenomas. A PPARD agonist (GW501516) enhanced APC{Delta}580 mutation-driven CRC, while a PPARD antagonist (GSK3787) suppressed it. Functional proteomics analyses and subsequent validation studies uncovered PPARD upregulation of multiple pro-invasive pathways that drive CRC progression (e.g. PDGFR{beta}, AKT1, CDK1 and EIF4G1). Our results identify novel mechanisms by which PPARD promotes CRC invasiveness and provide the rational for the development of PPARD antagonists to suppress CRC.
Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.