Effects of centrifugation on gonadal and adrenocortical steroids in rats

Many endocrine systems are sensitive to external changes in the environment. Both the pituitary adrenal and pituitary gonadal systems are affected by stress including centrifugation stress. The effect of centrifugation on the pituitary gonadal and pituitary adrenocortical systems was examined by measuring the gonadal and adrenal steroids in the plasma and brain following different duration and intensity of centrifugation stress in rats. Two studies were completed and the results are presented. The second study was carried out to describe the developmental changes of brain, plasma and testicular testosterone and dihydrotestosterone in Sprague Dawley rats so that the effect of centrifugation stress on the pituitary gonadal syatem could be better evaluated in future studies

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

Expression-Based Genome-Wide Association Study Links Vitamin D-Binding Protein With Autoantigenicity in Type 1 Diabetes.

Type 1 diabetes (T1D) is caused by autoreactive T cells that recognize pancreatic islet antigens and destroy insulin-producing β-cells. This attack results from a breakdown in tolerance for self-antigens, which is controlled by ectopic antigen expression in the thymus and pancreatic lymph nodes (PLNs). The autoantigens known to be involved include a set of islet proteins, such as insulin, GAD65, IA-2, and ZnT8. In an attempt to identify additional antigenic proteins, we performed an expression-based genome-wide association study using microarray data from 118 arrays of the thymus and PLNs of T1D mice. We ranked all 16,089 protein-coding genes by the likelihood of finding repeated differential expression and the degree of tissue specificity for pancreatic islets. The top autoantigen candidate was vitamin D-binding protein (VDBP). T-cell proliferation assays showed stronger T-cell reactivity to VDBP compared with control stimulations. Higher levels and frequencies of serum anti-VDBP autoantibodies (VDBP-Abs) were identified in patients with T1D (n = 331) than in healthy control subjects (n = 77). Serum vitamin D levels were negatively correlated with VDBP-Ab levels in patients in whom T1D developed during the winter. Immunohistochemical localization revealed that VDBP was specifically expressed in α-cells of pancreatic islets. We propose that VDBP could be an autoantigen in T1D

Heterogeneity in HIV and cellular transcription profiles in cell line models of latent and productive infection: implications for HIV latency.

BackgroundHIV-infected cell lines are widely used to study latent HIV infection, which is considered the main barrier to HIV cure. We hypothesized that these cell lines differ from each other and from cells from HIV-infected individuals in the mechanisms underlying latency.ResultsTo quantify the degree to which HIV expression is inhibited by blocks at different stages of HIV transcription, we employed a recently-described panel of RT-ddPCR assays to measure levels of 7 HIV transcripts ("read-through," initiated, 5' elongated, mid-transcribed/unspliced [Pol], distal-transcribed [Nef], polyadenylated, and multiply-sliced [Tat-Rev]) in bulk populations of latently-infected (U1, ACH-2, J-Lat) and productively-infected (8E5, activated J-Lat) cell lines. To assess single-cell variation and investigate cellular genes associated with HIV transcriptional blocks, we developed a novel multiplex qPCR panel and quantified single cell levels of 7 HIV targets and 89 cellular transcripts in latently- and productively-infected cell lines. The bulk cell HIV transcription profile differed dramatically between cell lines and cells from ART-suppressed individuals. Compared to cells from ART-suppressed individuals, latent cell lines showed lower levels of HIV transcriptional initiation and higher levels of polyadenylation and splicing. ACH-2 and J-Lat cells showed different forms of transcriptional interference, while U1 cells showed a block to elongation. Single-cell studies revealed marked variation between/within cell lines in expression of HIV transcripts, T cell phenotypic markers, antiviral factors, and genes implicated in latency. Expression of multiply-spliced HIV Tat-Rev was associated with expression of cellular genes involved in activation, tissue retention, T cell transcription, and apoptosis/survival.ConclusionsHIV-infected cell lines differ from each other and from cells from ART-treated individuals in the mechanisms governing latent HIV infection. These differences in viral and cellular gene expression must be considered when gauging the suitability of a given cell line for future research on HIV. At the same time, some features were shared across cell lines, such as low expression of antiviral defense genes and a relationship between productive infection and genes involved in survival. These features may contribute to HIV latency or persistence in vivo, and deserve further study using novel single cell assays such as those described in this manuscript

Multiplex meta-analysis of RNA expression to identify genes with variants associated with immune dysfunction

ObjectiveWe demonstrate a genome-wide method for the integration of many studies of gene expression of phenotypically similar disease processes, a method of multiplex meta-analysis. We use immune dysfunction as an example disease process.DesignWe use a heterogeneous collection of datasets across human and mice samples from a range of tissues and different forms of immunodeficiency. We developed a method integrating Tibshirani's modified t-test (SAM) is used to interrogate differential expression within a study and Fisher's method for omnibus meta-analysis to identify differentially expressed genes across studies. The ability of this overall gene expression profile to prioritize disease associated genes is evaluated by comparing against the results of a recent genome wide association study for common variable immunodeficiency (CVID).ResultsOur approach is able to prioritize genes associated with immunodeficiency in general (area under the ROC curve = 0.713) and CVID in particular (area under the ROC curve = 0.643).ConclusionsThis approach may be used to investigate a larger range of failures of the immune system. Our method may be extended to other disease processes, using RNA levels to prioritize genes likely to contain disease associated DNA variants

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.Comment: 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 200

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research

A Classifier-based approach to identify genetic similarities between diseases

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease

Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease

Motivation: Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possible interaction in context of disease. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies. Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data