The mycobacteriophages, phages that infect the genus Mycobacterium, display profound genetic diversity and widespread geographical distribution, and possess significant medical and ecological importance. However, most of the majority of functions of mycobacteriophage proteins and the identity of most genetic regulatory elements remain unknown. We characterized the gene expression profile of Kampy, a cluster A4 mycobacteriophage, during infection of its host, Mycobacterium smegmatis, using RNA-Seq and mass spectrometry. We show that mycobacteriophage Kampy transcription occurs in roughly two phases, an early phase consisting of genes for metabolism, DNA synthesis, and gene regulation, and a late phase consisting of structural genes and lysis genes. Additionally, we identify the earliest genes transcribed during infection, along with several other possible regulatory units not obvious from inspection of Kampy's genomic structure. The transcriptional profile of Kampy appears similar to that of mycobacteriophage TM4 but unlike that of mycobacteriophage Giles, a result which further expands our understanding of the diversity of mycobacteriophage gene expression programs during infection.
Methylmercury (MeHg) is a widespread environmental toxin that preferentially and adversely affects developing organisms. To investigate the impact of MeHg toxicity on the formation of the vertebrate nervous system at physiologically relevant concentrations, we designed a graded phenotype scale for evaluating Xenopus laevis embryos exposed to MeHg in solution. Embryos displayed a range of abnormalities in response to MeHg, particularly in brain development, which is influenced by both MeHg concentration and the number of embryos per ml of exposure solution. A TC50 of ~50 μg/l and LC50 of ~100 μg/l were found when maintaining embryos at a density of one per ml, and both increased with increasing embryo density. In situ hybridization and microarray analysis showed no significant change in expression of early neural patterning genes including sox2, en2, or delta; however a noticeable decrease was observed in the terminal neural differentiation genes GAD and xGAT, but not xVGlut. PCNA, a marker for proliferating cells, was negatively correlated with MeHg dose, with a significant reduction in cell number in the forebrain and spinal cord of exposed embryos by tadpole stages. Conversely, the number of apoptotic cells in neural regions detected by a TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labeling) assay was significantly increased. These results provide evidence that disruption of embryonic neural development by MeHg may not be directly due to a loss of neural progenitor specification and gene transcription, but to a more general decrease in cell proliferation and increase in cell death throughout the developing nervous system.
Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
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