Guided genome halving: hardness, heuristics and the history of the Hemiascomycetes

Motivation: Some present day species have incurred a whole genome doubling event in their evolutionary history, and this is reflected today in patterns of duplicated segments scattered throughout their chromosomes. These duplications may be used as data to ‘halve’ the genome, i.e. to reconstruct the ancestral genome at the moment of doubling, but the solution is often highly nonunique. To resolve this problem, we take account of outgroups, external reference genomes, to guide and narrow down the search

Epithelial cell–derived secreted and transmembrane 1a signals to activated neutrophils during pneumococcal pneumonia

Airway epithelial cell responses are critical to the outcome of lung infection. In this study, we aimed to identify unique contributions of epithelial cells during lung infection. To differentiate genes induced selectively in epithelial cells during pneumonia, we compared genome-wide expression profiles from three sorted cell populations: epithelial cells from uninfected mouse lungs, epithelial cells from mouse lungs with pneumococcal pneumonia, and nonepithelial cells from those same infected lungs. Of 1,166 transcripts that were more abundant in epithelial cells from infected lungs compared with nonepithelial cells from the same lungs or from epithelial cells of uninfected lungs, 32 genes were identified as highly expressed secreted products. Especially strong signals included two related secreted and transmembrane (Sectm) 1 genes, Sectm1a and Sectm1b. Refinement of sorting strategies suggested that both Sectm1 products were induced predominantly in conducting airway epithelial cells. Sectm1 was induced during the early stages of pneumococcal pneumonia, and mutation of NF-kB RelA in epithelial cells did not diminish its expression. Instead, type I IFN signaling was necessary and sufficient for Sectm1 induction in lung epithelial cells, mediated by signal transducer and activator of transcription 1. For target cells, Sectm1a bound to myeloid cells preferentially, in particular Ly6GbrightCD11bbright neutrophils in the infected lung. In contrast, Sectm1a did not bind to neutrophils from uninfected lungs. Sectm1a increased expression of the neutrophil-attracting chemokine CXCL2 by neutrophils from the infected lung. We propose that Sectm1a is an epithelial product that sustains a positive feedback loop amplifying neutrophilic inflammation during pneumococcal pneumonia

Distinct gene signatures in aortic tissue from ApoE-/- mice exposed to pathogens or Western diet

BACKGROUND: Atherosclerosis is a progressive disease characterized by inflammation and accumulation of lipids in vascular tissue. Porphyromonas gingivalis (Pg) and Chlamydia pneumoniae (Cp) are associated with inflammatory atherosclerosis in humans. Similar to endogenous mediators arising from excessive dietary lipids, these Gram-negative pathogens are pro-atherogenic in animal models, although the specific inflammatory/atherogenic pathways induced by these stimuli are not well defined. In this study, we identified gene expression profiles that characterize P. gingivalis, C. pneumoniae, and Western diet (WD) at acute and chronic time points in aortas of Apolipoprotein E (ApoE-/-) mice. RESULTS: At the chronic time point, we observed that P. gingivalis was associated with a high number of unique differentially expressed genes compared to C. pneumoniae or WD. For the top 500 differentially expressed genes unique to each group, we observed a high percentage (76%) that exhibited decreased expression in P. gingivalis-treated mice in contrast to a high percentage (96%) that exhibited increased expression in WD mice. C. pneumoniae treatment resulted in approximately equal numbers of genes that exhibited increased and decreased expression. Gene Set Enrichment Analysis (GSEA) revealed distinct stimuli-associated phenotypes, including decreased expression of mitochondrion, glucose metabolism, and PPAR pathways in response to P. gingivalis but increased expression of mitochondrion, lipid metabolism, carbohydrate and amino acid metabolism, and PPAR pathways in response to C. pneumoniae; WD was associated with increased expression of immune and inflammatory pathways. DAVID analysis of gene clusters identified by two-way ANOVA at acute and chronic time points revealed a set of core genes that exhibited altered expression during the natural progression of atherosclerosis in ApoE-/- mice; these changes were enhanced in P. gingivalis-treated mice but attenuated in C. pneumoniae-treated mice. Notable differences in the expression of genes associated with unstable plaques were also observed among the three pro-atherogenic stimuli. CONCLUSIONS: Despite the common outcome of P. gingivalis, C. pneumoniae, and WD on the induction of vascular inflammation and atherosclerosis, distinct gene signatures and pathways unique to each pro-atherogenic stimulus were identified. Our results suggest that pathogen exposure results in dysregulated cellular responses that may impact plaque progression and regression pathways

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization\u27s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (\u3e90% sensitivity; \u3e70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Background: The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results: Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions: The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Genome-Wide Analyses of Nkx2-1 Binding to Transcriptional Target Genes Uncover Novel Regulatory Patterns Conserved in Lung Development and Tumors

The homeodomain transcription factor Nkx2-1 is essential for normal lung development and homeostasis. In lung tumors, it is considered a lineage survival oncogene and prognostic factor depending on its expression levels. The target genes directly bound by Nkx2-1, that could be the primary effectors of its functions in the different cellular contexts where it is expressed, are mostly unknown. In embryonic day 11.5 (E11.5) mouse lung, epithelial cells expressing Nkx2-1 are predominantly expanding, and in E19.5 prenatal lungs, Nkx2-1-expressing cells are predominantly differentiating in preparation for birth. To evaluate Nkx2-1 regulated networks in these two cell contexts, we analyzed genome-wide binding of Nkx2-1 to DNA regulatory regions by chromatin immunoprecipitation followed by tiling array analysis, and intersected these data to expression data sets. We further determined expression patterns of Nkx2-1 developmental target genes in human lung tumors and correlated their expression levels to that of endogenous NKX2-1. In these studies we uncovered differential Nkx2-1 regulated networks in early and late lung development, and a direct function of Nkx2-1 in regulation of the cell cycle by controlling the expression of proliferation-related genes. New targets, validated in Nkx2-1 shRNA transduced cell lines, include E2f3, Cyclin B1, Cyclin B2, and c-Met. Expression levels of Nkx2-1 direct target genes identified in mouse development significantly correlate or anti-correlate to the levels of endogenous NKX2-1 in a dosage-dependent manner in multiple human lung tumor expression data sets, supporting alternative roles for Nkx2-1 as a transcriptional activator or repressor, and direct regulator of cell cycle progression in development and tumors