Mechanism of Rapid Transcriptional Induction of Tumor Necrosis Factor Alpha-Responsive Genes by NF-κB

NF-κB induces the expression of genes involved in immune response, apoptosis, inflammation, and the cell cycle. Certain NF-κB-responsive genes are activated rapidly after the cell is stimulated by cytokines and other extracellular signals. However, the mechanism by which these genes are activated is not entirely understood. Here we report that even though NF-κB interacts directly with TAF(II)s, induction of NF-κB by tumor necrosis factor alpha (TNF-α) does not enhance TFIID recruitment and preinitiation complex formation on some NF-κB-responsive promoters. These promoters are bound by the transcription apparatus prior to TNF-α stimulus. Using the immediate-early TNF-α-responsive gene A20 as a prototype promoter, we found that the constitutive association of the general transcription apparatus is mediated by Sp1 and that this is crucial for rapid transcriptional induction by NF-κB. In vitro transcription assays confirmed that NF-κB plays a postinitiation role since it enhances the transcription reinitiation rate whereas Sp1 is required for the initiation step. Thus, the consecutive effects of Sp1 and NF-κB on the transcription process underlie the mechanism of their synergy and allow rapid transcriptional induction in response to cytokines

Sensing Cellular Metabolic Activity via a Molecular-Controlled Semiconductor Resistor

Over the last decade, we have developed a molecular-controlled semiconductor resistor (MOCSER) device that is highly sensitive to variations in its surface potentials. This device was applied as a molecular sensor both in the gas phase and in solutions. The device is based on an AlGaAs/GaAs structure. In the current work, we developed an electronic biosensor for real-time, label-free monitoring of cellular metabolic activity by culturing HeLa cells directly on top of the device’s conductive channel. Several properties of GaAs make it attractive for developing biosensors, among others its high electron mobility and ability to control the device’s properties by proper epitaxial growing. However, GaAs is very reactive and sensitive to oxidation in aqueous solutions, and its arsenic residues are highly toxic. Nevertheless, we have managed to overcome this inherent chemical instability by developing a surface-protecting layer using polymerized (3-mercaptopropyl)-trimethoxysilane (MPTMS). To improve cell adhesion and biocompatibility, the MPTMS-coated devices were further modified with an additional layer of (3-aminopropyl)-trimethoxysilane (APTMS). HeLa cells were found to grow successfully on these devices, and MOCSER devices cultured with these cells were stable and sensitive to cellular metabolic activity. The sensitivity of the MOCSER device results from the sensing of extracellular acidification in the microenvironment of the cell-MOCSER interspace. We have found that this sensitivity is maintained only when the device is partially covered with the cellular layer, whereas at full coverage the sensitivity is lost. This phenomenon is related to the negatively charged cellular membrane potentials that lead to a reduction in the channel’s conductivity. We propose that the coated MOCSER device can be applied for real-time and continuous monitoring of cellular viability and activity

Differential Regulation of NF-κB by Elongation Factors Is Determined by Core Promoter Type▿

NF-κB transcription factors activate genes important for immune response, inflammation, and cell survival. P-TEFb and DSIF, which are positive and negative transcription elongation factors, respectively, both regulate NF-κB-induced transcription, but the mechanism underlying their recruitment to NF-κB target genes is unknown. We show here that upon induction of NF-κB, a subset of target genes is regulated differentially by either P-TEFb or DSIF. The regulation of these genes and their occupancy by these elongation factors are dependent on the NF-κB enhancer and the core promoter type. Converting a TATA-less promoter to a TATA promoter switches the regulation of NF-κB from DSIF to P-TEFb. Accumulation or displacement of DSIF and P-TEFb is dictated by the formation of distinct initiation complexes (TFIID dependent or independent) on the two types of core promoter. The underlying mechanism for the dissociation of DSIF from TATA promoters upon NF-κB activation involves the phosphorylation of RNA polymerase II by P-TEFb. The results highlight a regulatory link between the initiation and the elongation phases of the transcription reaction and broaden our comprehension of the NF-κB pathway

Exploring differential exon usage via short- and long-read RNA sequencing strategies

Alternative splicing produces various mRNAs, and thereby various protein products, from one gene, impacting a wide range of cellular activities. However, accurate reconstruction and quantification of full-length transcripts using short-reads is limited, due to their length. Long-reads sequencing technologies may provide a solution by sequencing full-length transcripts. We explored the use of both Illumina short-reads and two long Oxford Nanopore Technology (cDNA and Direct RNA) RNA-Seq reads for detecting global differential splicing during mouse embryonic stem cell differentiation, applying several bioinformatics strategies: gene-based, isoform-based and exon-based. We detected the strongest similarity among the sequencing platforms at the gene level compared to exon-based and isoform-based. Furthermore, the exon-based strategy discovered many differential exon usage (DEU) events, mostly in a platform-dependent manner and in non-differentially expressed genes. Thus, the platforms complemented each other in the ability to detect DEUs (i.e. long-reads exhibited an advantage in detecting DEUs at the UTRs, and short-reads detected more DEUs). Exons within 20 genes, detected in one or more platforms, were here validated by PCR, including key differentiation genes, such as Mdb3 and Aplp1. We provide an important analysis resource for discovering transcriptome changes during stem cell differentiation and insights for analysing such data

Using Synthetic Mouse Spike-In Transcripts to Evaluate RNA-Seq Analysis Tools

<div><p>One of the key applications of next-generation sequencing (NGS) technologies is RNA-Seq for transcriptome genome-wide analysis. Although multiple studies have evaluated and benchmarked RNA-Seq tools dedicated to gene level analysis, few studies have assessed their effectiveness on the transcript-isoform level. Alternative splicing is a naturally occurring phenomenon in <a href="http://en.wikipedia.org/wiki/Eukaryote" target="_blank">eukaryotes</a>, significantly increasing the <a href="http://en.wikipedia.org/wiki/Biodiversity" target="_blank">biodiversity</a> of proteins that can be encoded by the genome. The aim of this study was to assess and compare the ability of the bioinformatics approaches and tools to assemble, quantify and detect differentially expressed transcripts using RNA-Seq data, in a controlled experiment. To this end, <i>in vitro</i> synthesized mouse spike-in control transcripts were added to the total RNA of differentiating mouse embryonic bodies, and their expression patterns were measured. This novel approach was used to assess the accuracy of the tools, as established by comparing the observed results versus the results expected of the mouse controlled spiked-in transcripts. We found that detection of differential expression at the gene level is adequate, yet on the transcript-isoform level, all tools tested lacked <a href="http://en.wikipedia.org/wiki/Accuracy_and_precision" target="_blank">accuracy and precision</a>.</p></div

Linear regression model using normalized RSEM FPKM values for all spike-ins.

<p>Linear regression model using normalized RSEM FPKM values for all spike-ins.</p

Analysis of ERCC spike-ins.

<p>Differential expression of ERCC spike-ins. (A) Scatter plot of DESeq2 normalized ratio between ERCC mix2 and ERCC mix 1 versus the expected ratio for day 0. Note the axes scale in the plot is logarithmic. The correlation coefficient and the slope were calculated on the log values. The gray lines represent 95% confidence interval of the slope. The slope confidence interval is indicated in brackets. (B) Scatter plot illustrating the relationship between the spike-in levels and differential expression detection capacities. The fold changes between the two ERCC mixes and the FDR were calculated using DESeq2. The maximum concentration (log₁₀) within the pairwise comparison is shown in the X- axis. (C) Number of significantly differentially expressed (DE) ERCC spikes.</p

Experiment and analysis design.

<p>(A) Illustration of the experimental sample preparation. The experiment consisted of four RNA samples, two replicates (designated as Rep) of Day 0 (colored in blue) and two of Day 4 (green), each sample was divided to four vials, to two of them ERCC mix 1 was added and to other two mix 2 (orange). In addition one of the custom IVT spike-ins was added to each vial (violet). In total there were 16 vials. Custom mix 4 is the control without IVT spike-ins. (B) Diagram of the bioinformatics analysis design. (C) Genome browser view depicting the gene locus of Pomc that includes three distinct spike-in transcripts (AK030714, AK017581 and AK017492).</p