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  • Genomics  (18)
  • Physiology & Biochemistry
  • Oxford University Press  (20)
  • Elsevier
  • 2015-2019  (20)
  • 2017  (2)
  • 2015  (18)
  • 1
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    Vitamin C targets (p)ppGpp synthesis leading to stalling of long-term survival and biofilm formation in Mycobacterium smegmatis (2017)
    Oxford University Press
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    Publication Date: 2017-01-13
    Description: Earlier, vitamin C was demonstrated to sterilize Mycobacterium tuberculosis culture via Fenton's reaction at high concentration. It alters the regulatory pathways associated with stress response and dormancy. Since (p)ppGpp is considered to be the master regulator of stress response and is responsible for bacterial survival under stress, we tested the effect of vitamin C on the formation of (p)ppGpp. In vivo estimation of (p)ppGpp showed a decrease in (p)ppGpp levels in vitamin C-treated M. smegmatis cells in comparison to the untreated cells. Furthermore, in vitro (p)ppGpp synthesis using Rel MSM enzyme was conducted in order to confirm the specificity of the inhibition in the presence of variable concentrations of vitamin C. We observed that vitamin C at high concentration can inhibit the synthesis of (p)ppGpp. We illustrated binding of vitamin C to Rel MSM by isothermal titration calorimetry. Enzyme kinetics was followed where K 0.5 was found to be increased with the concomitant reduction of V max value suggesting mixed inhibition. Both long-term survival and biofilm formation were inhibited by vitamin C. The experiments suggest that vitamin C has the potential to be developed as the inhibitor of (p)ppGpp synthesis and stress response, at least in the concentration range used here.
    Keywords: Physiology & Biochemistry
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
    Published by Oxford University Press
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  • 2
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    A role of nitrite reductase (NirBD) for NO homeostatic regulation in Streptomyces coelicolor A3(2) (2017)
    Oxford University Press
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    Publication Date: 2017-01-19
    Description: Although nitric oxide (NO) is an important signaling molecule in bacteria and higher organisms, excessive intracellular NO is highly reactive and dangerous. Therefore, living cells need strict regulation systems for cellular NO homeostasis. Recently, we discovered that Streptomyces coelicolor A3(2) retains the nitrogen oxide cycle (NO 3 – -〉NO 2 – -〉NO-〉NO 3 – ) and nitrite removal system. The nitrogen oxide cycle regulates cellular NO levels, thereby controlling secondary metabolism initiation (red-pigmented antibiotic, RED production) and morphological differentiation. Nitrite induces gene expression in neighboring cells, suggesting another role for this cycle as a producer of transmittable intercellular communication molecules. Here, we demonstrated that ammonium-producing nitrite reductase (NirBD) is involved in regulating NO homeostasis in S. coelicolor A3(2). NirBD was constitutively produced in culture independently of GlnR, a known transcriptional factor. NirBD cleared the accumulated nitrite from the medium. Nir deletion mutants showed increased NO-dependent gene expression at later culture stages, whereas the wild-type M145 showed decreased expression, suggesting that high NO concentration was maintained in the mutant. Moreover, the nir deletion mutant produced more RED than that produced by the wild-type M145. These results suggest that NO 2 – removal by NirBD is important to regulate NO homeostasis and to complete NO signaling in S. coelicolor .
    Keywords: Physiology & Biochemistry
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
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  • 3
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    Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations (2015)
    Oxford University Press
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    Publication Date: 2015-09-19
    Description: Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction of genome-wide haplotypes based on sequence overlap information. Therefore, we present EVORhA, a haplotype reconstruction method that complements phasing information in the non-empty read overlap with the frequency estimations of inferred local haplotypes. As was shown with simulated data, as soon as read lengths and/or mutation rates become restrictive for state-of-the-art methods, the use of this additional frequency information allows EVORhA to still reliably reconstruct genome-wide haplotypes. On real data, we show the applicability of the method in reconstructing the population composition of evolved bacterial populations and in decomposing mixed bacterial infections from clinical samples.
    Keywords: Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 4
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    Longitudinal epigenetic and gene expression profiles analyzed by three-component analysis reveal down-regulation of genes involved in protein translation in human aging (2015)
    Oxford University Press
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    Publication Date: 2015-08-29
    Description: Data on biological mechanisms of aging are mostly obtained from cross-sectional study designs. An inherent disadvantage of this design is that inter-individual differences can mask small but biologically significant age-dependent changes. A serially sampled design (same individual at different time points) would overcome this problem but is often limited by the relatively small numbers of available paired samples and the statistics being used. To overcome these limitations, we have developed a new vector-based approach, termed three-component analysis, which incorporates temporal distance, signal intensity and variance into one single score for gene ranking and is combined with gene set enrichment analysis. We tested our method on a unique age-based sample set of human skin fibroblasts and combined genome-wide transcription, DNA methylation and histone methylation (H3K4me3 and H3K27me3) data. Importantly, our method can now for the first time demonstrate a clear age-dependent decrease in expression of genes coding for proteins involved in translation and ribosome function. Using analogies with data from lower organisms, we propose a model where age-dependent down-regulation of protein translation-related components contributes to extend human lifespan.
    Keywords: Genomics
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    Topics: Biology
    Published by Oxford University Press
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  • 5
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    Accurate read-based metagenome characterization using a hierarchical suite of unique signatures (2015)
    Oxford University Press
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    Publication Date: 2015-05-29
    Description: A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines G enomic O rigins T hrough T axonomic CHA llenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.
    Keywords: Genomics
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    Topics: Biology
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  • 6
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    Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform (2015)
    Oxford University Press
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    Publication Date: 2015-04-02
    Description: With read lengths of currently up to 2 x 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.
    Keywords: Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 7
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    iRNA-seq: computational method for genome-wide assessment of acute transcriptional regulation from total RNA-seq data (2015)
    Oxford University Press
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    Publication Date: 2015-04-02
    Description: RNA-seq is a sensitive and accurate technique to compare steady-state levels of RNA between different cellular states. However, as it does not provide an account of transcriptional activity per se , other technologies are needed to more precisely determine acute transcriptional responses. Here, we have developed an easy, sensitive and accurate novel computational method, iRNA-seq , for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data. Comparison of the results derived from iRNA-seq analyses with parallel results derived using current methods for genome-wide determination of transcriptional activity, i.e. global run-on (GRO)-seq and RNA polymerase II (RNAPII) ChIP-seq, demonstrate that iRNA-seq provides similar results in terms of number of regulated genes and their fold change. However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material. In conclusion, iRNA-seq offers an attractive novel alternative to current methods for determination of changes in transcriptional activity at a genome-wide level.
    Keywords: Genomics
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    Topics: Biology
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  • 8
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    Haploinsufficiency predictions without study bias (2015)
    Oxford University Press
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    Publication Date: 2015-08-29
    Description: Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants.
    Keywords: Genomics
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    Topics: Biology
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  • 9
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    Robust meta-analysis of gene expression using the elastic net (2015)
    Oxford University Press
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    Publication Date: 2015-07-12
    Description: Meta-analysis of gene expression has enabled numerous insights into biological systems, but current methods have several limitations. We developed a method to perform a meta-analysis using the elastic net, a powerful and versatile approach for classification and regression. To demonstrate the utility of our method, we conducted a meta-analysis of lung cancer gene expression based on publicly available data. Using 629 samples from five data sets, we trained a multinomial classifier to distinguish between four lung cancer subtypes. Our meta-analysis-derived classifier included 58 genes and achieved 91% accuracy on leave-one-study-out cross-validation and on three independent data sets. Our method makes meta-analysis of gene expression more systematic and expands the range of questions that a meta-analysis can be used to address. As the amount of publicly available gene expression data continues to grow, our method will be an effective tool to help distill these data into knowledge.
    Keywords: Genomics
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    Topics: Biology
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  • 10
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    Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles (2015)
    Oxford University Press
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    Publication Date: 2015-04-21
    Description: Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact .
    Keywords: Genomics
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  • 11
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    ChiNet uncovers rewired transcription subnetworks in tolerant yeast for advanced biofuels conversion (2015)
    Oxford University Press
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    Publication Date: 2015-05-20
    Description: Analysis of rewired upstream subnetworks impacting downstream differential gene expression aids the delineation of evolving molecular mechanisms. Cumulative statistics based on conventional differential correlation are limited for subnetwork rewiring analysis since rewiring is not necessarily equivalent to change in correlation coefficients. Here we present a computational method ChiNet to quantify subnetwork rewiring by statistical heterogeneity that enables detection of potential genotype changes causing altered transcription regulation in evolving organisms. Given a differentially expressed downstream gene set, ChiNet backtracks a rewired upstream subnetwork from a super-network including gene interactions known to occur under various molecular contexts. We benchmarked ChiNet for its high accuracy in distinguishing rewired artificial subnetworks, in silico yeast transcription-metabolic subnetworks, and rewired transcription subnetworks for Candida albicans versus Saccharomyces cerevisiae , against two differential-correlation based subnetwork rewiring approaches. Then, using transcriptome data from tolerant S. cerevisiae strain NRRL Y-50049 and a wild-type intolerant strain, ChiNet identified 44 metabolic pathways affected by rewired transcription subnetworks anchored to major adaptively activated transcription factor genes YAP1 , RPN4 , SFP1 and ROX1 , in response to toxic chemical challenges involved in lignocellulose-to-biofuels conversion. These findings support the use of ChiNet in rewiring analysis of subnetworks where differential interaction patterns resulting from divergent nonlinear dynamics abound.
    Keywords: Genomics
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  • 12
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    Assembly constraints drive co-evolution among ribosomal constituents (2015)
    Oxford University Press
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    Publication Date: 2015-06-24
    Description: Ribosome biogenesis, a central and essential cellular process, occurs through sequential association and mutual co-folding of protein–RNA constituents in a well-defined assembly pathway. Here, we construct a network of co-evolving nucleotide/amino acid residues within the ribosome and demonstrate that assembly constraints are strong predictors of co-evolutionary patterns. Predictors of co-evolution include a wide spectrum of structural reconstitution events, such as cooperativity phenomenon, protein-induced rRNA reconstitutions, molecular packing of different rRNA domains, protein–rRNA recognition, etc. A correlation between folding rate of small globular proteins and their topological features is known. We have introduced an analogous topological characteristic for co-evolutionary network of ribosome, which allows us to differentiate between rRNA regions subjected to rapid reconstitutions from those hindered by kinetic traps. Furthermore, co-evolutionary patterns provide a biological basis for deleterious mutation sites and further allow prediction of potential antibiotic targeting sites. Understanding assembly pathways of multicomponent macromolecules remains a key challenge in biophysics. Our study provides a ‘proof of concept’ that directly relates co-evolution to biophysical interactions during multicomponent assembly and suggests predictive power to identify candidates for critical functional interactions as well as for assembly-blocking antibiotic target sites.
    Keywords: Genomics
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  • 13
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    Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins (2015)
    Oxford University Press
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    Publication Date: 2015-02-18
    Description: The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates’ recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins , implemented in Python and C and supported on Linux and Mac OS X.
    Keywords: Genomics
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  • 14
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    BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers (2015)
    Oxford University Press
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    Publication Date: 2015-02-18
    Description: Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.
    Keywords: Genomics
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  • 15
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    Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data (2015)
    Oxford University Press
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    Publication Date: 2015-12-02
    Description: Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide.
    Keywords: Genomics
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  • 16
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    EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization (2015)
    Oxford University Press
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    Publication Date: 2015-03-14
    Description: Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.
    Keywords: Genomics
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  • 17
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    Base-resolution methylation patterns accurately predict transcription factor bindings in vivo (2015)
    Oxford University Press
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    Publication Date: 2015-03-14
    Description: Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo . However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.
    Keywords: Genomics
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  • 18
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    Allele-specific copy-number discovery from whole-genome and whole-exome sequencing (2015)
    Oxford University Press
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    Publication Date: 2015-08-18
    Description: Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/ .
    Keywords: Genomics
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  • 19
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    Microbial species delineation using whole genome sequences (2015)
    Oxford University Press
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    Publication Date: 2015-08-18
    Description: Increased sequencing of microbial genomes has revealed that prevailing prokaryotic species assignments can be inconsistent with whole genome information for a significant number of species. The long-standing need for a systematic and scalable species assignment technique can be met by the genome-wide Average Nucleotide Identity (gANI) metric, which is widely acknowledged as a robust measure of genomic relatedness. In this work, we demonstrate that the combination of gANI and the alignment fraction (AF) between two genomes accurately reflects their genomic relatedness. We introduce an efficient implementation of AF,gANI and discuss its successful application to 86.5M genome pairs between 13,151 prokaryotic genomes assigned to 3032 species. Subsequently, by comparing the genome clusters obtained from complete linkage clustering of these pairs to existing taxonomy, we observed that nearly 18% of all prokaryotic species suffer from anomalies in species definition. Our results can be used to explore central questions such as whether microorganisms form a continuum of genetic diversity or distinct species represented by distinct genetic signatures. We propose that this precise and objective AF,gANI-based species definition: the MiSI (Microbial Species Identifier) method, be used to address previous inconsistencies in species classification and as the primary guide for new taxonomic species assignment, supplemented by the traditional polyphasic approach, as required.
    Keywords: Genomics
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  • 20
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    Redefining the transcriptional regulatory dynamics of classically and alternatively activated macrophages by deepCAGE transcriptomics (2015)
    Oxford University Press
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    Publication Date: 2015-08-18
    Description: Classically or alternatively activated macrophages (M1 and M2, respectively) play distinct and important roles for microbiocidal activity, regulation of inflammation and tissue homeostasis. Despite this, their transcriptional regulatory dynamics are poorly understood. Using promoter-level expression profiling by non-biased deepCAGE we have studied the transcriptional dynamics of classically and alternatively activated macrophages. Transcription factor (TF) binding motif activity analysis revealed four motifs, NFKB1_REL_RELA, IRF1,2, IRF7 and TBP that are commonly activated but have distinct activity dynamics in M1 and M2 activation. We observe matching changes in the expression profiles of the corresponding TFs and show that only a restricted set of TFs change expression. There is an overall drastic and transient up-regulation in M1 and a weaker and more sustainable up-regulation in M2. Novel TFs, such as Thap6, Maff , (M1) and Hivep1, Nfil3, Prdm1 , (M2) among others, were suggested to be involved in the activation processes. Additionally, 52 (M1) and 67 (M2) novel differentially expressed genes and, for the first time, several differentially expressed long non-coding RNA (lncRNA) transcriptome markers were identified. In conclusion, the finding of novel motifs, TFs and protein-coding and lncRNA genes is an important step forward to fully understand the transcriptional machinery of macrophage activation.
    Keywords: Genomics
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