Project description:Singapore grouper iridovirus (SGIV) is the major agent that causes severe iridovirus diseases in grouper maricluture. Based on the genomic information, a DNA microarray, containing probes corresponding to 162 putative SGIV open reading frames (ORFs) was constucted. The viral microarrays wereused to classify the majority of SGIV transcripts into three temporal kinetic classes (immediate-early, IE; early, E; late, L) during an in vitro infection by their dependence on de novo protein synthesis inhibitor and viral DNA replication. Keywords: drug response
Project description:Singapore grouper iridovirus (SGIV) is the major agent that causes severe iridovirus diseases in grouper maricluture. Based on the genomic information, a DNA microarray, containing probes corresponding to 162 putative SGIV open reading frames (ORFs) was constucted. The viral microarrays wereused to classify the majority of SGIV transcripts into three temporal kinetic classes (immediate-early, IE; early, E; late, L) during an in vitro infection by their dependence on de novo protein synthesis inhibitor and viral DNA replication. Keywords: drug response To map the SGIV transcripts into temporal kinetic classes during the infection in vitro, GS cells were treated with drug inhibitors as previously described with some slight modifications. Briefly, GS monolayers were treated for 1 h prior to, during and throughout the viral infection with either CHX or PAA, which inhibited de novo protein synthesis and viral DNA replication mechanisms, respectively. To distinguish between viral IE transcripts and other transcripts, cells infected with SGIV (MOI of 5) or mock infected were treated with CHX (100 µg/ml), and then harvested at 8 h p.i. Whereas, viral E and L transcripts were categorized when cells infected with SGIV (MOI of 5) in the presence and absence of PAA (200 µg/ml), respectively. Until at 36 h p.i., cells were collected for RNA extraction. In a comparison analysis, we used Significance Analysis of Microarrays (SAM) software to identify the groups of different expression genes between the drug-treated samples and untreated samples. SAM identifies genes with statistically significant changes in expression by assimilating a set of gene-specific t tests. Significance is based on an estimated of FDRâ¤5% and a 2 fold-change cutoff. Only the genes that met the following three criteria could be categorized into various temporal kinetic classes: (i) a viral gene was considered to be a IE transcript if its expression level increased or weakly affected under CHX treatment, (ii) a viral gene was considered to be a L transcript if its median intensity ratio in the PAA-treated samples was at least two-fold lower than that in the untreated samples and (iii) a viral gene was considered to be a E transcript if it expressed higher or remained within the two- fold range after PAA treatment but was inhibited distinctly by CHX treatment. To identify the significant differences in gene expression, a criterion with at least two-fold change combined with studentâs one sample t-test p value <0.05 was adopted.
Project description:We combined RNA metabolic labeling and alkylation with droplet-based sequencing to detect newly synthesized mRNAs in single cells. With the classification of labeled and unlabeled precursor and mature mRNAs, we modeled and analyzed the time-dependent RNA kinetic rates associated with the cell cycle. We found both transcription and degradation rates are highly dynamic over the cell cycle and different kinetic regulation types were observed for cycling genes.
Project description:Lung adenocarcinoma (LUAD) is one of the most common pathological and histological subtypes of primary lung cancer, with high morbidity and mortality. MicroRNAs (miRNAs) are endogenous small non-coding RNAs that regulate the expression of genes at post-transcriptional level. It was reported that A-to-I miRNA editing was decreased in tumors, suggesting the potential value of miRNA editing in cancer classification. However, existing miRNA-based cancer classification models mainly used the frequencies of miRNAs. In order to validate the contribution of miRNA editing information in cancer classification, we extracted three types of miRNA features, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that four classification algorithms selected, i.e., kNN, C4.5, RF and SVM, generally had better performances on all features than on the abundances of miRNAs alone. Since the number of features were large, we used three feature selection (FS) methods to further improve the classification models. One of the FS methods, the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-miR-182 48u (an edited miRNA), from 316 training samples. And all of the four classification algorithms achieved 100% accuracy on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing are useful in improving the classification of LUAD samples. And the three miRNAs selected by DFL potentially represent an effective molecular signature for LUAD diagnosis.
Project description:The mesenchyme consists of heterogeneous cell populations that support neighboring structures and are integral to intercellular signaling. Despite such importance, mesenchymal cell types are poorly defined morphologically and molecularly, lagging behind their counterparts in the epithelial, endothelial, and immune lineages. Leveraging single-cell RNA-seq, three-dimensional imaging, and lineage tracing, we classify the mouse lung mesenchyme into three proximal-distal axes that are associated with the endothelium, epithelium, and interstitium, respectively. From proximal to distal, (1) the vascular axis includes vascular smooth muscle cells and pericytes that transition as arterioles and venules ramify into capillaries; (2) the epithelial axis includes airway smooth muscle cells and two populations of myofibroblasts: ductal myofibroblasts, surrounding alveolar ducts and marked by CDH4, HHIP, and LGR6, which persist post-alveologenesis, and alveolar myofibroblasts, surrounding alveoli and marked by high expression of PDGFRA, which undergo developmental apoptosis; (3) the interstitial axis, residing between the epithelial and vascular trees and sharing a newly-identified marker MEOX2, includes fibroblasts in the bronchovascular bundle and the alveolar interstitium that are marked by IL33 and Wnt2, respectively. Single-cell imaging reveals distinct morphology of each mesenchymal cell population. This classification provides a conceptual and experimental framework applicable to other organs.
Project description:Direct sampling of building wastewater has the potential to enable precision public health observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic “features”, but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features—providing orthogonal information to physicochemical properties—illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.
Project description:poly-A RNA profiling of Drosophila intermediate neural porgenitors (INPs) of three different temporal states reveal genes involved in temporal patterning