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Motivation: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes., Results: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression., Availability: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.

DNA microarrays provide a global view of the physiological state of the cell by parallel analysis of the expression levels of all the genes in an organism. The effects of four bactericidal agents on the expression pattern of Escherichia coli MG1655 were assessed. Compounds were chosen on the basis of their different mechanisms of action and included inhibitors of DNA replication and recombination, translation, transcription and cell wall biosynthesis. The addition of rifampin resulted in increased expression of the target, rpoB, as well as several genes involved in nucleotide salvage and purine biosynthesis. The addition of ampicillin resulted in overall changes in gene expression that showed some similarity to changes induced by rifampin. The addition of the antibiotics kanamycin or norfloxacin resulted in the induction of unique gene expression signatures: a heat shock response to kanamycin and an SOS response to norfloxacin. Several genes of unknown function showed expression profiles similar to the genes associated with the SOS or the heat shock response. Thus, these profiles define families of genes with similar expression phenotypes that can be tested for related function., (Copyright 2003 S. Karger AG, Basel)

Radix Astragali is widely used in the traditional Chinese medicine with the effect of antiaging. The purpose of this study is to explore the main active ingredients and targets of Radix Astragali against renal aging by network pharmacology and further to verify the mechanism of the main active ingredients in vitro. TCMSP, ETCM, and TCMID databases were used to screen active ingredients of Radix Astragali. Targets of active ingredients were predicted using BATMAN-TCM and cross validated using kidney aging-related genes obtained from GeneCards and NCBI database. Pathways enrichment and protein-protein interaction (PPI) analysis were performed on core targets. Additionally, a pharmacological network was constructed based on the active ingredients-targets-pathways. HK-2 cell was treated with D-galactose to generate a cell model of senescence. CCK-8 and β-galactosidase were used to detect the effect of Radix Astragali active components on cell proliferation and aging. ELISA was used to detect the expression of senescence-associated secreted protein (TGF-β and IL-6) in the cell culture supernatant. Western blot was used to detect the expression of key proteins in the SIRT1/ p 53 pathway. Five active ingredients (Astragaloside I, II, III, IV and choline) were identified from Radix Astragali, and all these active ingredients target a total of 128 genes. Enrichment analysis showed these genes were implicated in 153 KEGG pathways, including the p 53 , FoxO, and AMPK pathway. 117 proteins and 572 interactions were found in PPI network. TP53 and SIRT1 were two hub genes in PPI network, which interacted with each other. The pharmacological network showed that the five main active ingredients target on some coincident genes, including TP53 and SIRT1. These targeted genes were involved in the p53, FoxO, and AMPK pathway. Proliferation of HK-2 cells was increased by Astragaloside IV treatment compared with that of the D-Gal treatment group. However, the proliferation of the SA-β-gal positive cells were inhibited. The expression of TGF-β and IL-6 in the D-Gal group was higher than that in the normal group, and the treatment of Astragaloside IV could significantly reduce the expression of TGF-β and IL-6. The expression of SIRT1 in the Astragaloside IV group was higher than that in the D-Gal group. However, the expression of p 53 and p 21 was less in the Astragaloside IV group than that in the D-Gal group. This study suggested that Astragaloside IV is an important active ingredient of Radix Astragali in the treatment of kidney aging via the SITR1- p 53 pathway. [ABSTRACT FROM AUTHOR]

Motivation Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. Results In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expression from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological features in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expression. Interestingly, we found the genes with higher fold change can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attention scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSIs. [ABSTRACT FROM AUTHOR]

RORγt and RORα are transcription factors of the RAR-related orphan nuclear receptor (ROR) family. They are expressed in Th17 cells and have been suggested to play a role in Th17 differentiation. Although RORγt signature genes have been characterized in mouse Th17 cells, detailed information on its transcriptional control in human Th17 cells is limited and even less is known about RORα signature genes which have not been reported in either human or mouse T cells. In this study, global gene expression of human CD4 T cells activated under Th17 skewing conditions was profiled by RNA sequencing. RORγt and RORα signature genes were identified in these Th17 cells treated with specific siRNAs to knock down RORγt or RORα expression. We have generated selective small molecule RORγt modulators and they were also utilized as pharmacological tools in RORγt signature gene identification. Our results showed that RORγt controlled the expression of a very selective number of genes in Th17 cells and most of them were regulated by RORα as well albeit a weaker influence. Key Th17 genes including IL-17A, IL-17F, IL-23R, CCL20 and CCR6 were shown to be regulated by both RORγt and RORα. Our results demonstrated an overlapping role of RORγt and RORα in human Th17 cell differentiation through regulation of a defined common set of Th17 genes. RORγt as a drug target for treatment of Th17 mediated autoimmune diseases such as psoriasis has been demonstrated recently in clinical trials. Our results suggest that RORα could be involved in same disease mechanisms and gene signatures identified in this report could be valuable biomarkers for tracking the pharmacodynamic effects of compounds that modulate RORγt or RORα activities in patients. [ABSTRACT FROM AUTHOR]

RNA-seq technology has become an important tool for quantifying the gene and transcript expression in transcriptome study. The two major difficulties for the gene and transcript expression quantification are the read mapping ambiguity and the overdispersion of the read distribution along reference sequence. Many approaches have been proposed to deal with these difficulties. A number of existing methods use Poisson distribution to model the read counts and this easily splits the counts into the contributions from multiple transcripts. Meanwhile, various solutions were put forward to account for the overdispersion in the Poisson models. By checking the similarities among the variation patterns of read counts for individual genes, we found that the count variation is exon-specific and has the conserved pattern across the samples for each individual gene. We introduce Gamma-distributed latent variables to model the read sequencing preference for each exon. These variables are embedded to the rate parameter of a Poisson model to account for the overdispersion of read distribution. The model is tractable since the Gamma priors can be integrated out in the maximum likelihood estimation. We evaluate the proposed approach, PGseq, using four real datasets and one simulated dataset, and compare its performance with other popular methods. Results show that PGseq presents competitive performance compared to other alternatives in terms of accuracy in the gene and transcript expression calculation and in the downstream differential expression analysis. Especially, we show the advantage of our method in the analysis of low expression. [ABSTRACT FROM AUTHOR]

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis. [ABSTRACT FROM AUTHOR]

Formation of free radicals and other reactive molecules is responsible for the adverse effects produced by a number of hepatotoxic compounds. cDNA microarray technology was used to compare transcriptional profiles elicited by training and testing sets of 15 oxidant stressors/reactive metabolite treatments to those produced by approximately 85 other paradigm compounds (mostly hepatotoxicants) to determine a shared signature profile for oxidant stress-associated hepatotoxicity. Initially, 100 genes were chosen that responded significantly different to oxidant stressors/reactive metabolites (OS/RM) compared to other samples in the database, then a 25-gene subset was selected by multivariate analysis. Many of the selected genes (e.g., aflatoxin aldehyde reductase, diaphorase, epoxide hydrolase, heme oxgenase and several glutathione transferases) are well-characterized oxidant stress/Nrf-2-responsive genes. Less than 10 other compounds co-cluster with our training and testing set compounds and these are known to generate OS/RMs as part of their mechanisms of toxicity. Using OS/RM signature gene sets, compounds previously associated with macrophage activation formed a distinct cluster separate from OS/RM and other compounds. A 69-gene set was chosen to maximally separate compounds in control, macrophage activator, peroxisome proliferator and OS/RM classes. The ease with which these ‘oxidative stressor’ classes can be separated indicates a role for microarray technology in early prediction and classification of hepatotoxicants. The ability to rapidly screen the oxidant stress potential of compounds may aid in avoidance of some idiosyncratic drug reactions as well as overtly toxic compounds. [Copyright &y& Elsevier]

Information on the neuroanatomical expression of a given gene is critical to understanding its function in the central nervous system. The integration of laser capture microdissection (LCM), T7-based RNA amplification and cDNA microarrays allows for this information to be simultaneously generated for thousands of genes. To validate this integrative approach, we catalogued the gene expression profiles of seven rat brain nuclei or subnuclei. A hundred cells from the following seven brain nuclei were analyzed: locus coeruleus (LC), dorsal raphe nucleus (DR), parvocellular division (PA) and magnocellular division (MG) of the hypothalamic paraventricular nucleus (PVN) and CA1, CA3 and dentate gyrus (DG) divisions of the hippocampal formation. Of the 2145 genes investigated, 1402 genes (65%) gave a hybridization signal statistically different from the background level that was defined by non-specific hybridizations to 15 different plant genes. Validation of our microarray data on four arbitrarily selected genes was confirmed by Real-Time PCR. Previous research showing expression patterns of ‘signature’ genes (n=17) for specific brain nuclei are consistent with our findings. For example, as previously shown, enriched mRNA expression encoding the serotonin transporter or tyrosine hydroxylase was found in DR and LC cells, respectively. Interestingly, expression of the serotonin 5-HT2B receptor mRNA was also found in DR cells. We confirmed this new finding by in-situ hybridization. The hierarchical clustering analysis of gene expression shows that the two divisions of the PVN (PA and MG) are closely related to each other, as well as the three regions of the hippocampal formation (CA1, CA3 and DG), which also showed similar gene expression profiles. This study demonstrates the importance, feasibility and utility of cellular brain nuclei profiling. [Copyright &y& Elsevier]