The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

Antisense Transcription in Loci Associated to Hereditary Neurodegenerative Diseases

Natural antisense transcripts are common features of mammalian genes providing additional regulatory layers of gene expression. A comprehensive description of antisense transcription in loci associated to familial neurodegenerative diseases may identify key players in gene regulation and provide tools for manipulating gene expression. We take advantage of the FANTOM5 sequencing datasets that represent the largest collection to date of genome-wide promoter usage in almost 2000 human samples. Transcription start sites (TSSs) are mapped at high resolution by the use of a modified protocol of cap analysis of gene expression (CAGE) for high-throughput single molecule next-generation sequencing with Helicos (hCAGE). Here we present the analysis of antisense transcription at 17 loci associated to hereditary Alzheimer’s disease, Frontotemporal Dementia, Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Huntington’s disease. We focused our analysis on libraries derived from brain tissues and primary cells. We also screened libraries from total blood and blood cell populations in the quest for peripheral biomarkers of neurodegenerative diseases. We identified 63 robust promoters in antisense orientation to genes associated to familial neurodegeneration. When applying a less stringent cutoff, this number increases to over 400. A subset of these promoters represents alternative TSSs for 24 FANTOM5 annotated long noncoding RNA (lncRNA) genes, in antisense orientation to 13 of the loci analyzed here, while the remaining contribute to the expression of additional transcript variants. Intersection with GWAS studies, sample ontology, and dynamic expression reveals association to specific genetic traits as well as cell and tissue types, not limited to neurodegenerative diseases. Antisense transcription was validated for a subset of genes, including those encoding for Microtubule-Associated Protein Tau, α-synuclein, Parkinsonism-associated deglycase DJ-1, and Leucin-Rich Repeat Kinase 2. This work provides evidence for the existence of additional regulatory mechanisms of the expression of neurodegenerative disease-causing genes by previously not-annotated and/or not-validated antisense long noncoding RNAs.

Multilayer regulation of CD4 T cell subset differentiation in the era of single cell genomics

CD4 T cells are major immune cell types that mediate effector responses appropriate for diverse incoming threats. These cells have been categorized into different subsets based on how they are induced, expression of specific master transcription factors, and the resulting effector cell phenotypes as defined by expression of signature cytokines. However, recent studies assessing the expression of gene modules in single CD4 T cells, rather than expression of one or a few signature genes, have provided a more complex picture in which the canonical model does not fit as cleanly as proposed. Here, we review the concepts of lineage commitment, plasticity and functional heterogeneity in the context of this greater complexity. We then apply our current understanding of CD4 T cell subsets to discuss outstanding questions regarding follicular helper T cells and follicular regulatory T cells with respect to their shared features with other known CD4 T cell subsets.

Networks of mRNA Processing and Alternative Splicing Regulation in Health and Disease

mRNA processing events introduce an intricate layer of complexity into gene expression processes, supporting a tremendous level of diversification of the genome's coding and regulatory potential, particularly in vertebrate species. The recent development of massive parallel sequencing methods and their adaptation to the identification and quantification of different RNA species and the dynamics of mRNA metabolism and processing has generated an unprecedented view over the regulatory networks that are established at this level, which contribute to sustain developmental, tissue specific or disease specific gene expression programs. In this chapter, we provide an overview of the recent evolution of transcriptome profiling methods and the surprising insights that have emerged in recent years regarding distinct mRNA processing events - from the 5' end to the 3' end of the molecule.

MicroRNAs-361-5p and miR-574-5p associate with human adipose morphology and regulate EBF1 expression in white adipose tissue

Reduced adipose expression of the transcription factor Early B cell factor 1 (EBF1) is linked to white adipose tissue (WAT) hypertrophy. We aimed to identify microRNAs (miRNAs) associated with WAT hypertrophy and EBF1 regulation. We mapped WAT miRNA expression from 26 non-obese women discordant in WAT morphology and determined EBF1 activity in the non-obese and 30 obese women. Expression of 15 miRNAs was higher in hypertrophy and 10 were predicted to target EBF1. Binding of miR-365-5p/miR-574-5p were validated with 3'-UTR assay. Overexpression of miR-365-5p or miR-574-5p reduced EBF1 while inhibition of miR-574 increased EBF1 expression in human adipocytes in vitro. Additive effects on EBF1 were observed when concomitantly overexpressing both miRNAs. EBF1 targets were affected by over expression/inhibition of either miRNAs. Finally, miR-365-5p/miR-574-5p expression in 56 individuals correlated significantly with EBF1 activity. Our results suggest that miR-365-5p and miR-574-5p may be linked to WAT hypertrophy via effects on EBF1 expression.

Promoter Usage and Dynamics in Vascular Smooth Muscle Cells Exposed to Fibroblast Growth Factor-2 or Interleukin-1β

Smooth muscle cells (SMC) in blood vessels are normally growth quiescent and transcriptionally inactive. Our objective was to understand promoter usage and dynamics in SMC acutely exposed to a prototypic growth factor or pro-inflammatory cytokine. Using cap analysis gene expression (FANTOM5 project) we report differences in promoter dynamics for immediate-early genes (IEG) and other genes when SMC are exposed to fibroblast growth factor-2 or interleukin-1β. Of the 1871 promoters responding to FGF2 or IL-1β considerably more responded to FGF2 (68.4%) than IL-1β (18.5%) and 13.2% responded to both. Expression clustering reveals sets of genes induced, repressed or unchanged. Among IEG responding rapidly to FGF2 or IL-1β were FOS, FOSB and EGR-1, which mediates human SMC migration. Motif activity response analysis (MARA) indicates most transcription factor binding motifs in response to FGF2 were associated with a sharp induction at 1 h, whereas in response to IL-1β, most motifs were associated with a biphasic change peaking generally later. MARA revealed motifs for FOS_FOS{B,L1}_JUN{B,D} and EGR-1..3 in the cluster peaking 1 h after FGF2 exposure whereas these motifs were in clusters peaking 1 h or later in response to IL-1β. Our findings interrogating CAGE data demonstrate important differences in promoter usage and dynamics in SMC exposed to FGF2 or IL-1β.

Transcriptional and temporal response of Populus stems to gravi-stimulation

Plants modify development in response to external stimuli, to produce new growth that is appropriate for environmental conditions. For example, gravi-stimulation of leaning branches in angiosperm trees results in modifications of wood development, to produce tension wood that pulls leaning stems upright. Here, we use gravi-stimulation and tension wood response to dissect the temporal changes in gene expression underlying wood formation in Populus stems. Using time-series analysis of seven time points over a 14-d experiment, we identified 8,919 genes that were differentially expressed between tension wood (upper) and opposite wood (lower) sides of leaning stems. Clustering of differentially expressed genes showed four major transcriptional responses, including gene clusters whose transcript levels were associated with two types of tissue-specific impulse responses that peaked at about 24-48 h, and gene clusters with sustained changes in transcript levels that persisted until the end of the 14-d experiment. Functional enrichment analysis of those clusters suggests they reflect temporal changes in pathways associated with hormone regulation, protein localization, cell wall biosynthesis and epigenetic processes. Time-series analysis of gene expression is an underutilized approach for dissecting complex developmental responses in plants, and can reveal gene clusters and mechanisms influencing development.

Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review)

There is a need for specific cell types in regenerative medicine and biological research. Frequently, specific cell types may not be easily obtained or the quantity obtained is insufficient for study. Therefore, reprogramming by the direct conversion (transdifferentiation) or re‑induction of induced pluripotent stem cells has been used to obtain cells expressing similar profiles to those of the desired types. Therefore, a specific cocktail of transcription factors (TFs) is required for induction. Nevertheless, identifying the correct combination of TFs is difficult. Although certain computational approaches have been proposed for this task, their methods are complex, and corresponding implementations are difficult to use and generalize for specific source or target cell types. In the present review four computational approaches that have been proposed to obtain likely TFs were compared and discussed. A simplified view of the computational complexity of these methods is provided that consists of three basic ideas: i) The definition of target and non‑target cell types; ii) the estimation of candidate TFs; and iii) filtering candidates. This simplified view was validated by analyzing a well‑documented cardiomyocyte differentiation. Subsequently, these reviewed methods were compared when applied to an unknown differentiation of corneal endothelial cells. The generated results may provide important insights for laboratory assays. Data and computer scripts that may assist with direct conversions in other cell types are also provided.

Automated transition analysis of activated gene regulation during diauxic nutrient shift in Escherichia coli and adipocyte differentiation in mouse cells

Background

Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.

Results

Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed.

Conclusions

Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.

Enhancer Activation by Pharmacologic Displacement of LSD1 from GFI1 Induces Differentiation in Acute Myeloid Leukemia

Pharmacologic inhibition of LSD1 promotes blast cell differentiation in acute myeloid leukemia (AML) with MLL translocations. The assumption has been that differentiation is induced through blockade of LSD1's histone demethylase activity. However, we observed that rapid, extensive, drug-induced changes in transcription occurred without genome-wide accumulation of the histone modifications targeted for demethylation by LSD1 at sites of LSD1 binding and that a demethylase-defective mutant rescued LSD1 knockdown AML cells as efficiently as wild-type protein. Rather, LSD1 inhibitors disrupt the interaction of LSD1 and RCOR1 with the SNAG-domain transcription repressor GFI1, which is bound to a discrete set of enhancers located close to transcription factor genes that regulate myeloid differentiation. Physical separation of LSD1/RCOR1 from GFI1 is required for drug-induced differentiation. The consequent inactivation of GFI1 leads to increased enhancer histone acetylation within hours, which directly correlates with the upregulation of nearby subordinate genes.

Enhancer RNA profiling predicts transcription factor activity

Transcription factors (TFs) exert their regulatory influence through the binding of enhancers, resulting in coordination of gene expression programs. Active enhancers are often characterized by the presence of short, unstable transcripts termed enhancer RNAs (eRNAs). While their function remains unclear, we demonstrate that eRNAs are a powerful readout of TF activity. We infer sites of eRNA origination across hundreds of publicly available nascent transcription data sets and show that eRNAs initiate from sites of TF binding. By quantifying the colocalization of TF binding motif instances and eRNA origins, we derive a simple statistic capable of inferring TF activity. In doing so, we uncover dozens of previously unexplored links between diverse stimuli and the TFs they affect.

Deep Cap Analysis of Gene Expression (CAGE): Genome-Wide Identification of Promoters, Quantification of Their Activity, and Transcriptional Network Inference

Among the most significant findings of the post-genomic era, the discovery of pervasive transcription of mammalian genomes has tremendously modified our understanding of the genome output seen as RNA molecules. The increased focus on non-protein-coding genomic regions together with concomitant technological innovations has led to rapid discovery of numerous noncoding transcripts (ncRNAs). Biological relevance and functional roles of the vast majority of these ncRNAs remain largely unknown.The cap analysis of gene expression (CAGE) technology allows accurate transcript detection and quantification without relying on preexisting transcript models. In combination with complementary data sets, generated using other technologies, it has been shown as an efficient approach for exploring transcriptome complexity.Here, we describe the use of CAGE for the identification of novel noncoding transcripts in mammalian cells providing detailed information for basic data processing and advanced bioinformatics analyses.

NanoCAGE: A Method for the Analysis of Coding and Noncoding 5'-Capped Transcriptomes

Transcripts in all eukaryotes are characterized by the 5'-end specific cap structure in mRNAs. Cap Analysis Gene Expression or CAGE makes use of these caps to specifically obtain cDNA fragments from the 5'-end of RNA and sequences those at high throughput for transcript identification and genome-wide mapping of transcription start sites for coding and noncoding genes. Here, we provide an improved version of our nanoCAGE protocol that has been developed for preparing CAGE libraries from as little as 50 ng of total RNA within three standard working days. Key steps in library preparation have been improved over our previously published protocol to obtain libraries having a good 5'-end selection and a more equal size distribution for higher sequencing efficiency on Illumina MiSeq and HiSeq sequencers. We recommend nanoCAGE as the method of choice for transcriptome profiling projects even from limited amounts of RNA, and as the best approach for genome-wide mapping of transcription start sites within promoter regions.

Integrated analysis of motif activity and gene expression changes of transcription factors

The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.

Monitoring transcription initiation activities in rat and dog

The promoter landscape of several non-human model organisms is far from complete. As a part of FANTOM5 data collection, we generated 13 profiles of transcription initiation activities in dog and rat aortic smooth muscle cells, mesenchymal stem cells and hepatocytes by employing CAGE (Cap Analysis of Gene Expression) technology combined with single molecule sequencing. Our analyses show that the CAGE profiles recapitulate known transcription start sites (TSSs) consistently, in addition to uncover novel TSSs. Our dataset can be thus used with high confidence to support gene annotation in dog and rat species. We identified 28,497 and 23,147 CAGE peaks, or promoter regions, for rat and dog respectively, and associated them to known genes. This approach could be seen as a standard method for improvement of existing gene models, as well as discovery of novel genes. Given that the FANTOM5 data collection includes dog and rat matched cell types in human and mouse as well, this data would also be useful for cross-species studies.

STAT3 and STAT6 Signaling Pathways Synergize to Promote Cathepsin Secretion from Macrophages via IRE1α Activation

Tumor-associated macrophages play critical roles during tumor progression by promoting angiogenesis, cancer cell proliferation, invasion, and metastasis. Cysteine cathepsin proteases, produced by macrophages and cancer cells, modulate these processes, but it remains unclear how these typically lysosomal enzymes are regulated and secreted within the tumor microenvironment. Here, we identify a STAT3 and STAT6 synergy that potently upregulates cathepsin secretion by macrophages via engagement of an unfolded protein response (UPR) pathway. Whole-genome expression analyses revealed that the TH2 cytokine interleukin (IL)-4 synergizes with IL-6 or IL-10 to activate UPR via STAT6 and STAT3. Pharmacological inhibition of the UPR sensor IRE1α blocks cathepsin secretion and blunts macrophage-mediated cancer cell invasion. Similarly, genetic deletion of STAT3 and STAT6 signaling components impairs tumor development and invasion in vivo. Together, these findings demonstrate that cytokine-activated STAT3 and STAT6 cooperate in macrophages to promote a secretory phenotype that enhances tumor progression in a cathepsin-dependent manner.

Targeting acute myeloid leukemia by drug-induced c-MYB degradation

Despite advances in our understanding of the molecular basis for particular subtypes of acute myeloid leukemia (AML), effective therapy remains a challenge for many individuals suffering from this disease. A significant proportion of both pediatric and adult AML patients cannot be cured and since the upper limits of chemotherapy intensification have been reached, there is an urgent need for novel therapeutic approaches. The transcription factor c-MYB has been shown to play a central role in the development and progression of AML driven by several different oncogenes, including mixed lineage leukemia (MLL)-fusion genes. Here, we have used a c-MYB gene expression signature from MLL-rearranged AML to probe the Connectivity Map database and identified mebendazole as a c-MYB targeting drug. Mebendazole induces c-MYB degradation via the proteasome by interfering with the heat shock protein 70 (HSP70) chaperone system. Transient exposure to mebendazole is sufficient to inhibit colony formation by AML cells, but not normal cord blood-derived cells. Furthermore, mebendazole is effective at impairing AML progression in vivo in mouse xenotransplantation experiments. In the context of widespread human use of mebendazole, our data indicate that mebendazole-induced c-MYB degradation represents a safe and novel therapeutic approach for AML.

Transcription start site profiling of 15 anatomical regions of the Macaca mulatta central nervous system

Rhesus macaque was the second non-human primate whose genome has been fully sequenced and is one of the most used model organisms to study human biology and disease, thanks to the close evolutionary relationship between the two species. But compared to human, where several previously unknown RNAs have been uncovered, the macaque transcriptome is less studied. Publicly available RNA expression resources for macaque are limited, even for brain, which is highly relevant to study human cognitive abilities. In an effort to complement those resources, FANTOM5 profiled 15 distinct anatomical regions of the aged macaque central nervous system using Cap Analysis of Gene Expression, a high-resolution, annotation-independent technology that allows monitoring of transcription initiation events with high accuracy. We identified 25,869 CAGE peaks, representing bona fide promoters. For each peak we provide detailed annotation, expanding the landscape of ‘known’ macaque genes, and we show concrete examples on how to use the resulting data. We believe this data represents a useful resource to understand the central nervous system in macaque.

The Transcriptional Network Structure of a Myeloid Cell: A Computational Approach

Understanding the general principles underlying genetic regulation in eukaryotes is an incomplete and challenging endeavor. The lack of experimental information regarding the regulation of the whole set of transcription factors and their targets in different cell types is one of the main reasons to this incompleteness. So far, there is a small set of curated known interactions between transcription factors and their downstream genes. Here, we built a transcription factor network for human monocytic THP-1 myeloid cells based on the experimentally curated FANTOM4 database where nodes are genes and the experimental interactions correspond to links. We present the topological parameters which define the network as well as some global structural features and introduce a relative inuence parameter to quantify the relevance of a transcription factor in the context of induction of a phenotype. Genes like ZHX2, ADNP, or SMAD6 seem to be highly regulated to avoid an avalanche transcription event. We compare these results with those of RegulonDB, a highly curated transcriptional network for the prokaryotic organism E. coli, finding similarities between general hallmarks on both transcriptional programs. We believe that an approach, such as the one shown here, could help to understand the one regulation of transcription in eukaryotic cells.

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

The three-dimensional arrangement of the human genome comprises a complex network of structural and regulatory chromatin loops important for coordinating changes in transcription during human development. To better understand the mechanisms underlying context-specific 3D chromatin structure and transcription during cellular differentiation, we generated comprehensive in situ Hi-C maps of DNA loops in human monocytes and differentiated macrophages. We demonstrate that dynamic looping events are regulatory rather than structural in nature and uncover widespread coordination of dynamic enhancer activity at preformed and acquired DNA loops. Enhancer-bound loop formation and enhancer activation of preformed loops together form multi-loop activation hubs at key macrophage genes. Activation hubs connect 3.4 enhancers per promoter and exhibit a strong enrichment for activator protein 1 (AP-1)-binding events, suggesting that multi-loop activation hubs involving cell-type-specific transcription factors represent an important class of regulatory chromatin structures for the spatiotemporal control of transcription.

Long non-coding RNAs and prostate cancer

Long non‐coding RNAs (lncRNAs) are RNA transcripts larger than 200 nucleotides that do not code for proteins the aberrant expression of which has been documented in various types of cancer, including prostate cancer. Lack of appropriate sensitive and specific biomarkers for prostate cancer has led to overdiagnosis and overtreatment, making lncRNAs promising novel biomarkers as well as therapeutic targets for the disease. The present review attempts to summarize the current knowledge of lncRNA expression patterns and mechanisms in prostate cancer, which contribute to carcinogenesis. In particular, we focused on lncRNAs regulated by androgen receptor and expressed in castration‐resistant prostate cancer.

The FANTOM5 collection, a data series underpinning mammalian transcriptome atlases in diverse cell types

The latest project from the FANTOM consortium, an international collaborative effort initiated by RIKEN, generated atlases of transcriptomes, in particular promoters, transcribed enhancers, and long-noncoding RNAs, across a diverse set of mammalian cell types. Here, we introduce the FANTOM5 collection, bringing together data descriptors, articles and analyses of FANTOM5 data published across the Nature Research journals. Associated data are openly available for reuse by all.

FANTOM5 CAGE profiles of human and mouse samples

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

An integrated expression atlas of miRNAs and their promoters in human and mouse

MicroRNAs (miRNAs) are short non-coding RNAs with key roles in cellular regulation. As part of the fifth edition of the Functional Annotation of Mammalian Genome (FANTOM5) project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 492 short RNA (sRNA) libraries, with matching Cap Analysis Gene Expression (CAGE) data, from 396 human and 47 mouse RNA samples. Promoters were identified for 1,357 human and 804 mouse miRNAs and showed strong sequence conservation between species. We also found that primary and mature miRNA expression levels were correlated, allowing us to use the primary miRNA measurements as a proxy for mature miRNA levels in a total of 1,829 human and 1,029 mouse CAGE libraries. We thus provide a broad atlas of miRNA expression and promoters in primary mammalian cells, establishing a foundation for detailed analysis of miRNA expression patterns and transcriptional control regions.

Transcriptional mechanisms that control expression of the macrophage colony-stimulating factor receptor locus

The proliferation, differentiation, and survival of cells of the macrophage lineage depends upon signals from the macrophage colony-stimulating factor (CSF) receptor (CSF1R). CSF1R is expressed by embryonic macrophages and induced early in adult hematopoiesis, upon commitment of multipotent progenitors to the myeloid lineage. Transcriptional activation of CSF1R requires interaction between members of the E26 transformation-specific family of transcription factors (Ets) (notably PU.1), C/EBP, RUNX, AP-1/ATF, interferon regulatory factor (IRF), STAT, KLF, REL, FUS/TLS (fused in sarcoma/ranslocated in liposarcoma) families, and conserved regulatory elements within the mouse and human CSF1R locus. One element, the Fms-intronic regulatory element (FIRE), within intron 2, is conserved functionally across all the amniotes. Lineage commitment in multipotent progenitors also requires down-regulation of specific transcription factors such as MYB, FLI1, basic leucine zipper transcriptional factor ATF-like (BATF3), GATA-1, and PAX5 that contribute to differentiation of alternative lineages and repress CSF1R transcription. Many of these transcription factors regulate each other, interact at the protein level, and are themselves downstream targets of CSF1R signaling. Control of CSF1R transcription involves feed–forward and feedback signaling in which CSF1R is both a target and a participant; and dysregulation of CSF1R expression and/or function is associated with numerous pathological conditions. In this review, we describe the regulatory network behind CSF1R expression during differentiation and development of cells of the mononuclear phagocyte system.

Transcriptional Regulation and Macrophage Differentiation

Monocytes and macrophages are professional phagocytes that occupy specific niches in every tissue of the body. Their survival, proliferation, and differentiation are controlled by signals from the macrophage colony-stimulating factor receptor (CSF-1R) and its two ligands, CSF-1 and interleukin-34. In this review, we address the developmental and transcriptional relationships between hematopoietic progenitor cells, blood monocytes, and tissue macrophages as well as the distinctions from dendritic cells. A huge repertoire of receptors allows monocytes, tissue-resident macrophages, or pathology-associated macrophages to adapt to specific microenvironments. These processes create a broad spectrum of macrophages with different functions and individual effector capacities. The production of large transcriptomic data sets in mouse, human, and other species provides new insights into the mechanisms that underlie macrophage functional plasticity.

Developmental Control of NRAMP1 (SLC11A1) Expression in Professional Phagocytes

NRAMP1 (SLC11A1) is a professional phagocyte membrane importer of divalent metals that contributes to iron recycling at homeostasis and to nutritional immunity against infection. Analyses of data generated by several consortia and additional studies were integrated to hypothesize mechanisms restricting NRAMP1 expression to mature phagocytes. Results from various epigenetic and transcriptomic approaches were collected for mesodermal and hematopoietic cell types and compiled for combined analysis with results of genetic studies associating single nucleotide polymorphisms (SNPs) with variations in NRAMP1 expression (eQTLs). Analyses establish that NRAMP1 is part of an autonomous topologically associated domain delimited by ubiquitous CCCTC-binding factor (CTCF) sites. NRAMP1 locus contains five regulatory regions: a predicted super-enhancer (S-E) key to phagocyte-specific expression; the proximal promoter; two intronic areas, including 3' inhibitory elements that restrict expression during development; and a block of upstream sites possibly extending the S-E domain. Also the downstream region adjacent to the 3' CTCF locus boundary may regulate expression during hematopoiesis. Mobilization of the locus 14 predicted transcriptional regulatory elements occurs in three steps, beginning with hematopoiesis; at the onset of myelopoiesis and through myelo-monocytic differentiation. Basal expression level in mature phagocytes is further influenced by genetic variation, tissue environment, and in response to infections that induce various epigenetic memories depending on microorganism nature. Constitutively associated transcription factors (TFs) include CCAAT enhancer binding protein beta (C/EBPb), purine rich DNA binding protein (PU.1), early growth response 2 (EGR2) and signal transducer and activator of transcription 1 (STAT1) while hypoxia-inducible factors (HIFs) and interferon regulatory factor 1 (IRF1) may stimulate iron acquisition in pro-inflammatory conditions. Mouse orthologous locus is generally conserved; chromatin patterns typify a de novo myelo-monocytic gene whose expression is tightly controlled by TFs Pu.1, C/ebps and Irf8; Irf3 and nuclear factor NF-kappa-B p 65 subunit (RelA) regulate expression in inflammatory conditions. Functional differences in the determinants identified at these orthologous loci imply that species-specific mechanisms control gene expression.

A mouse tissue transcription factor atlas

Transcription factors (TFs) drive various biological processes ranging from embryonic development to carcinogenesis. Here, we employ a recently developed concatenated tandem array of consensus TF response elements (catTFRE) approach to profile the activated TFs in 24 adult and 8 fetal mouse tissues on proteome scale. A total of 941 TFs are quantitatively identified, representing over 60% of the TFs in the mouse genome. Using an integrated omics approach, we present a TF network in the major organs of the mouse, allowing data mining and generating knowledge to elucidate the roles of TFs in various biological processes, including tissue type maintenance and determining the general features of a physiological system. This study provides a landscape of TFs in mouse tissues that can be used to elucidate transcriptional regulatory specificity and programming and as a baseline that may facilitate understanding diseases that are regulated by TFs.

While we have abundant data for transcription factor (TF) binding sites and TF expression at the mRNA level, our knowledge of TFs at the protein level and their DNA-binding activities is sparser. Here, the authors address this by using the catTFRE approach to profile active TFs in 24 adult and 8 fetal mouse tissues, and presenting the TF networks in major mouse organs.

RNA-sequence analysis of gene expression from honeybees (Apis mellifera) infected with Nosema ceranae

Honeybees (Apis mellifera) are constantly subjected to many biotic stressors including parasites. This study examined honeybees infected with Nosema ceranae (N. ceranae). N. ceranae infection increases the bees energy requirements and may contribute to their decreased survival. RNA-seq was used to investigate gene expression at days 5, 10 and 15 Post Infection (P.I) with N. ceranae. The expression levels of genes, isoforms, alternative transcription start sites (TSS) and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation suggesting that bees use a range of tactics to cope with the stress of N. ceranae infection. N. ceranae infection may cause reduced immune function in the bees by: (i)disturbing the host amino acids metabolism (ii) down-regulating expression of antimicrobial peptides (iii) down-regulation of cuticle coatings and (iv) down-regulation of odorant binding proteins.

Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease

The FANTOM5 consortium utilised cap analysis of gene expression (CAGE) to provide an unprecedented insight into transcriptional regulation in human cells and tissues. In the current study, we have used CAGE-based transcriptional profiling on an extended dense time course of the response of human monocyte-derived macrophages grown in macrophage colony-stimulating factor (CSF1) to bacterial lipopolysaccharide (LPS). We propose that this system provides a model for the differentiation and adaptation of monocytes entering the intestinal lamina propria. The response to LPS is shown to be a cascade of successive waves of transient gene expression extending over at least 48 hours, with hundreds of positive and negative regulatory loops. Promoter analysis using motif activity response analysis (MARA) identified some of the transcription factors likely to be responsible for the temporal profile of transcriptional activation. Each LPS-inducible locus was associated with multiple inducible enhancers, and in each case, transient eRNA transcription at multiple sites detected by CAGE preceded the appearance of promoter-associated transcripts. LPS-inducible long non-coding RNAs were commonly associated with clusters of inducible enhancers. We used these data to re-examine the hundreds of loci associated with susceptibility to inflammatory bowel disease (IBD) in genome-wide association studies. Loci associated with IBD were strongly and specifically (relative to rheumatoid arthritis and unrelated traits) enriched for promoters that were regulated in monocyte differentiation or activation. Amongst previously-identified IBD susceptibility loci, the vast majority contained at least one promoter that was regulated in CSF1-dependent monocyte-macrophage transitions and/or in response to LPS. On this basis, we concluded that IBD loci are strongly-enriched for monocyte-specific genes, and identified at least 134 additional candidate genes associated with IBD susceptibility from reanalysis of published GWA studies. We propose that dysregulation of monocyte adaptation to the environment of the gastrointestinal mucosa is the key process leading to inflammatory bowel disease.

Macrophages are immune cells that form the first line of defense against pathogens, but also mediate tissue damage in inflammatory disease. Macrophages initiate inflammation by recognising and responding to components of bacterial cells. Macrophages of the wall of the gut are constantly replenished from the blood. Upon entering the intestine, newly-arrived cells modulate their response to stimuli derived from the bacteria in the wall of the gut. This process fails in chronic inflammatory bowel diseases (IBD). Both the major forms of IBD, Crohn’s disease and ulcerative colitis, run in families. The inheritance is complex, involving more than 200 different regions of the genome. We hypothesised that the genetic risk of IBD is associated specifically with altered regulation of genes that control the development of macrophages. In this study, we used the comprehensive transcriptome dataset produced by the FANTOM5 consortium to identify the sets of promoters and enhancers that are involved in adaptation of macrophages to the gut wall, their response to bacterial stimuli, and how their functions are integrated. A reanalysis of published genome-wide association data based upon regulated genes in monocytes as candidates strongly supports the view that susceptibility to IBD arises from a primary defect in macrophage differentiation.

A Core Regulatory Circuit in Glioblastoma Stem Cells Links MAPK Activation to a Transcriptional Program of Neural Stem Cell Identity

Glioblastoma, the most common primary malignant brain tumor, harbors a small population of tumor initiating cells (glioblastoma stem cells) that have many properties similar to neural stem cells. To investigate common regulatory networks in both neural and glioblastoma stem cells, we subjected both cell types to in-vitro differentiation conditions and measured global gene-expression changes using gene expression microarrays. Analysis of enriched transcription factor DNA-binding sites in the promoters of differentially expressed genes was used to reconstruct regulatory networks involved in differentiation. Computational predictions, which were biochemically validated, show an extensive overlap of regulatory circuitry between cell types including a network centered on the transcription factor KLF4. We further demonstrate that EGR1, a transcription factor previously shown to be downstream of the MAPK pathway, regulates KLF4 expression and that KLF4 in turn transcriptionally activates NOTCH as well as SOX2. These results demonstrate how known genomic alterations in glioma that induce constitutive activation of MAPK are transcriptionally linked to master regulators essential for neural stem cell identify.

Decoding transcriptional states in cancer

Gene regulatory networks determine cellular identity. In cancer, aberrations of gene networks are caused by driver mutations that often affect transcription factors and chromatin modifiers. Nevertheless, gene transcription in cancer follows the same cis-regulatory rules as normal cells, and cancer cells have served as convenient model systems to study transcriptional regulation. Tumours often show regulatory heterogeneity, with subpopulations of cells in different transcriptional states, which has important therapeutic implications. Here, we review recent experimental and computational techniques to reverse engineer cancer gene networks using transcriptome and epigenome data. New algorithms, data integration strategies, and increasing amounts of single cell genomics data provide exciting opportunities to model dynamic regulatory states at unprecedented resolution.

Leukemia Stem Cells Microenvironment

The dynamic interactions between leukemic cells and bone marrow (BM) cells in the leukemia BM microenvironment regulate leukemia stem cell (LSC) properties including localization, self-renewal, differentiation, and proliferation. Recent research of normal and leukemia BM microenvironments has revealed several key components of specific niches that provide a sanctuary where subpopulations of leukemia cells evade chemotherapy-induced death and acquire a drug-resistant phenotype, as well as the molecular pathways critical for microenvironment/leukemia interactions. Although the biology of LSCs shares many similarities with that of normal hematopoietic stem cells (HSCs), LSCs are able to outcompete HSCs and hijack BM niches. Increasing evidence indicates that these niches fuel the growth of leukemia cells and contribute to therapeutic resistance and the metastatic potential of leukemia cells by shielding LSCs. Not only "microenvironment-induced oncogenesis," but also a "malignancy-induced microenvironment" have been proposed. In this chapter, the key components and regulation of BM niches in leukemic BM is described. In addition, metabolic changes in LSCs, which are currently a subject of intense investigation, will also be discussed to understand LSC survival.

Computational methods for direct cell conversion

Directed cell conversion (or transdifferentiation) of one somatic cell-type to another can be achieved by ectopic expression of a set of transcription factors. Since the experimental identification of transcription factors for transdifferentiation is extremely time-consuming and expensive, there are still relatively few transdifferentiations achieved in comparison to the number of human cell-types. However, the growing volume of transcriptional data available and the recent introduction of data-driven algorithmic approaches that predict factors for transdifferentiation holds great promise for accelerating this field. Here we review those computational methods whose in-silico predictions have been experimentally validated, highlighting differences and similarities. Our analysis reveals that the factors predicted by each method tend to be different due to varying source cells used, gene expression quantification and algorithmic steps. We show these differences have an impact on the regulatory influences downstream, with some methods favoring transcription factors regulating developmental progression and others favoring factors regulating mature cell processes. These computational approaches offer a starting point to predict and test novel factors for transdifferentiation. We argue that collecting high-quality gene expression data from single-cells or pure cell-populations across a broader set of cell-types would be necessary to improve the quality and consistency of the in-silico predictions.

The ISMARA client

ISMARA ( ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client.

Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals

Upon the first publication of the fifth iteration of the Functional Annotation of Mammalian Genomes collaborative project, FANTOM5, we gathered a series of primary data and database systems into the FANTOM web resource (http://fantom.gsc.riken.jp) to facilitate researchers to explore transcriptional regulation and cellular states. In the course of the collaboration, primary data and analysis results have been expanded, and functionalities of the database systems enhanced. We believe that our data and web systems are invaluable resources, and we think the scientific community will benefit for this recent update to deepen their understanding of mammalian cellular organization. We introduce the contents of FANTOM5 here, report recent updates in the web resource and provide future perspectives.

TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions

Transcription factors (TFs) play a pivotal role in transcriptional regulation, making them crucial for cell survival and important biological functions. For the regulation of transcription, interactions of different regulatory proteins known as transcription co-factors (TcoFs) and TFs are essential in forming necessary protein complexes. Although TcoFs themselves do not bind DNA directly, their influence on transcriptional regulation and initiation, although indirect, has been shown to be significant, with the functionality of TFs strongly influenced by the presence of TcoFs. In the TcoF-DB v2 database, we collect information on TcoFs. In this article, we describe updates and improvements implemented in TcoF-DB v2. TcoF-DB v2 provides several new features that enables exploration of the roles of TcoFs. The content of the database has significantly expanded, and is enriched with information from Gene Ontology, biological pathways, diseases and molecular signatures. TcoF-DB v2 now includes many more TFs; has substantially increased the number of human TcoFs to 958, and now includes information on mouse (418 new TcoFs). TcoF-DB v2 enables the exploration of information on TcoFs and allows investigations into their influence on transcriptional regulation in humans and mice. TcoF-DB v2 can be accessed at http://tcofdb.org/.

Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state-space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical "external stimulus-induced information loss" model of biological systems.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

BTR: training asynchronous Boolean models using single-cell expression data

Background

Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present.

Results

Here we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights.

Conclusions

BTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1235-y) contains supplementary material, which is available to authorized users.

Multiple Independent Retroelement Insertions in the Promoter of a Stress Response Gene Have Variable Molecular and Functional Effects in Drosophila

Promoters are structurally and functionally diverse gene regulatory regions. The presence or absence of sequence motifs and the spacing between the motifs defines the properties of promoters. Recent alternative promoter usage analyses in Drosophila melanogaster revealed that transposable elements significantly contribute to promote diversity. In this work, we analyzed in detail one of the transposable element insertions, named FBti0019985, that has been co-opted to drive expression of CG18446, a candidate stress response gene. We analyzed strains from different natural populations and we found that besides FBti0019985, there are another eight independent transposable elements inserted in the proximal promoter region of CG18446. All nine insertions are solo-LTRs that belong to the roo family. We analyzed the sequence of the nine roo insertions and we investigated whether the different insertions were functionally equivalent by performing 5'-RACE, gene expression, and cold-stress survival experiments. We found that different insertions have different molecular and functional consequences. The exact position where the transposable elements are inserted matters, as they all showed highly conserved sequences but only two of the analyzed insertions provided alternative transcription start sites, and only the FBti0019985 insertion consistently affects CG18446 expression. The phenotypic consequences of the different insertions also vary: only FBti0019985 was associated with cold-stress tolerance. Interestingly, the only previous report of transposable elements inserting repeatedly and independently in a promoter region in D. melanogaster, were also located upstream of a stress response gene. Our results suggest that functional validation of individual structural variants is needed to resolve the complexity of insertion clusters.

Comparative Digital Gene Expression Analysis of the Arabidopsis Response to Volatiles Emitted by Bacillus amyloliquefaciens

Some plant growth-promoting rhizobacteria (PGPR) regulated plant growth and elicited plant basal immunity by volatiles. The response mechanism to the Bacillus amyloliquefaciens volatiles in plant has not been well studied. We conducted global gene expression profiling in Arabidopsis after treatment with Bacillus amyloliquefaciens FZB42 volatiles by Illumina Digital Gene Expression (DGE) profiling of different growth stages (seedling and mature) and tissues (leaves and roots). Compared with the control, 1,507 and 820 differentially expressed genes (DEGs) were identified in leaves and roots at the seedling stage, respectively, while 1,512 and 367 DEGs were identified in leaves and roots at the mature stage. Seventeen genes with different regulatory patterns were validated using quantitative RT-PCR. Numerous DEGs were enriched for plant hormones, cell wall modifications, and protection against stress situations, which suggests that volatiles have effects on plant growth and immunity. Moreover, analyzes of transcriptome difference in tissues and growth stage using DGE profiling showed that the plant response might be tissue-specific and/or growth stage-specific. Thus, genes encoding flavonoid biosynthesis were downregulated in leaves and upregulated in roots, thereby indicating tissue-specific responses to volatiles. Genes related to photosynthesis were downregulated at the seedling stage and upregulated at the mature stage, respectively, thereby suggesting growth period-specific responses. In addition, the emission of bacterial volatiles significantly induced killing of cells of other organism pathway with up-regulated genes in leaves and the other three pathways (defense response to nematode, cell morphogenesis involved in differentiation and trichoblast differentiation) with up-regulated genes were significantly enriched in roots. Interestingly, some important alterations in the expression of growth-related genes, metabolic pathways, defense response to biotic stress and hormone-related genes were firstly founded response to FZB42 volatiles.

Multiple Herbicide Resistance in Lolium multiflorum and Identification of Conserved Regulatory Elements of Herbicide Resistance Genes

Herbicide resistance is a ubiquitous challenge to herbicide sustainability and a looming threat to control weeds in crops. Recently four genes were found constituently over-expressed in herbicide resistant individuals of Lolium rigidum, a close relative of Lolium multiflorum. These include two cytochrome P450s, one nitronate monooxygenase and one glycosyl-transferase. Higher expressions of these four herbicide metabolism related (HMR) genes were also observed after herbicides exposure in the gene expression databases, indicating them as reliable markers. In order to get an overview of herbicidal resistance status of L. multiflorum L, 19 field populations were collected. Among these populations, four populations were found to be resistant to acetolactate synthase (ALS) inhibitors while three exhibited resistance to acetyl-CoA carboxylase (ACCase) inhibitors in our initial screening and dose response study. The genotyping showed the presence of mutations Trp-574-Leu and Ile-2041-Asn in ALS and ACCase, respectively, and qPCR experiments revealed the enhanced expression of HMR genes in individuals of certain resistant populations. Moreover, co-expression networks and promoter analyses of HMR genes in O. sativa and A. thaliana resulted in the identification of a cis-regulatory motif and zinc finger transcription factors. The identified transcription factors were highly expressed similar to HMR genes in response to xenobiotics whereas the identified motif is known to play a vital role in coping with environmental stresses and maintaining genome stability. Overall, our findings provide an important step forward toward a better understanding of metabolism-based herbicide resistance that can be utilized to devise novel strategies of weed management.

Asymmetric Regulation of Peripheral Genes by Two Transcriptional Regulatory Networks

Transcriptional regulatory network (TRN) reconstitution and deconstruction occur simultaneously during reprogramming; however, it remains unclear how the starting and targeting TRNs regulate the induction and suppression of peripheral genes. Here we analyzed the regulation using direct cell reprogramming from human dermal fibroblasts to monocytes as the platform. We simultaneously deconstructed fibroblastic TRN and reconstituted monocytic TRN; monocytic and fibroblastic gene expression were analyzed in comparison with that of fibroblastic TRN deconstruction only or monocytic TRN reconstitution only. Global gene expression analysis showed cross-regulation of TRNs. Detailed analysis revealed that knocking down fibroblastic TRN positively affected half of the upregulated monocytic genes, indicating that intrinsic fibroblastic TRN interfered with the expression of induced genes. In contrast, reconstitution of monocytic TRN showed neutral effects on the majority of fibroblastic gene downregulation. This study provides an explicit example that demonstrates how two networks together regulate gene expression during cell reprogramming processes and contributes to the elaborate exploration of TRNs.

Expression Specificity of Disease-Associated lncRNAs: Toward Personalized Medicine

Long noncoding RNAs (lncRNAs) perform diverse regulatory functions in transcription, translation' chromatin modification, and cellular organization. Misregulation of lncRNAs is found linked to various human diseases. Compared to protein-coding RNAs' lncRNAs are more specific to organs, tissues, cell types, developmental stages, and disease conditions' making them promising candidates as diagnostic and prognostic biomarkers and as gene therapy targets. The functional annotation of mammalian genome (FANTOM) consortium utilizes cap analysis of gene expression (CAGE) method to quantify genome-wide activities of promoters and enhancers of coding and noncoding RNAs across a large collection of human and mouse tissues' cell types' diseases, and time-courses. The project discovered widespread transcription of major lncRNA classes, including lncRNAs derived from enhancers' bidirectional promoters' antisense lncRNAs' and repetitive elements. Results from FANTOM project enable assessment of lncRNA expression specificity across tissue and disease conditions' based on differential promoter and enhancer usage. More than 85 % of disease-related SNPs are within noncoding regions and are strikingly overrepresented in enhancer and promoter regions, suggestive of the importance of lncRNA loci at these SNP harboring regions to human diseases. In this chapter' we discuss lncRNA expression specificity' review diverse functions of disease-associated lncRNAs' and present perspectives on their potential therapeutic applications for personalized medicine. The future development of lncRNA applications relies on technologies to identify and validate their functions' structures' and mechanisms. Comprehensive understanding of genome-wide interaction networks of lncRNAs with proteins, chromatins, and other RNAs in regulating cellular processes will allow personalized medicine to use lncRNAs as highly specific biomarkers in diagnosis' prognosis, and therapeutic targets.

SINEUPs: A new class of natural and synthetic antisense long non-coding RNAs that activate translation

Over the past 10 years, it has emerged that pervasive transcription in mammalian genomes has a tremendous impact on several biological functions. Most of transcribed RNAs are lncRNAs and repetitive elements. In this review, we will detail the discovery of a new functional class of natural and synthetic antisense lncRNAs that stimulate translation of sense mRNAs. These molecules have been named SINEUPs since their function requires the activity of an embedded inverted SINEB2 sequence to UP-regulate translation. Natural SINEUPs suggest that embedded Transposable Elements may represent functional domains in long non-coding RNAs. Synthetic SINEUPs may be designed by targeting the antisense sequence to the mRNA of choice representing the first scalable tool to increase protein synthesis of potentially any gene of interest. We will discuss potential applications of SINEUP technology in the field of molecular biology experiments, in protein manufacturing as well as in therapy of haploinsufficiencies.

Community Structure Reveals Biologically Functional Modules in MEF2C Transcriptional Regulatory Network

Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcription factor interactions downstream from MEF2C, a human transcription factor master regulator. By analyzing the connectivity structure of such network, we were able to find different biologically functional processes and specific biochemical pathways statistically enriched in communities of genes into the network, such processes are related to cell signaling, cell cycle and metabolism. In this way we further support the hypothesis that structural properties of biological networks encode an important part of their functional behavior in eukaryotic cells.

Towards understanding pre-mRNA splicing mechanisms and the role of SR proteins

Alternative pre-mRNA splicing provides a source of vast protein diversity by removing non-coding sequences (introns) and accurately linking different exonic regions in the correct reading frame. The regulation of alternative splicing is essential for various cellular functions in both pathological and physiological conditions. In eukaryotic cells, this process is commonly used to increase proteomic diversity and to control gene expression either co- or post-transcriptionally. Alternative splicing occurs within a megadalton-sized, multi-component machine consisting of RNA and proteins; during the splicing process, this complex undergoes dynamic changes via RNA-RNA, protein-protein and RNA-protein interactions. Co-transcriptional splicing functionally integrates the transcriptional machinery, thereby enabling the two processes to influence one another, whereas post-transcriptional splicing facilitates the coupling of RNA splicing with post-splicing events. This review addresses the structural aspects of spliceosomes and the mechanistic implications of their stepwise assembly on the regulation of pre-mRNA splicing. Moreover, the role of phosphorylation-based, signal-induced changes in the regulation of the splicing process is demonstrated.

Biological computational approaches: new hopes to improve (re)programming robustness, regenerative medicine and cancer therapeutics

Hundreds of transcription factors (TFs) are expressed and work in each cell type, but the identity of the cells is defined and maintained through the activity of a small number of core TFs. Existing reprogramming strategies predominantly focus on the ectopic expression of core TFs of an intended fate in a given cell type regardless of the state of native/somatic gene regulatory networks (GRNs) of the starting cells. Interestingly, an important point is that how much products of the reprogramming, transdifferentiation and differentiation (programming) are identical to their in vivo counterparts. There is evidence that shows that direct fate conversions of somatic cells are not complete, with target cell identity not fully achieved. Manipulation of core TFs provides a powerful tool for engineering cell fate in terms of extinguishment of native GRNs, the establishment of a new GRN, and preventing installation of aberrant GRNs. Conventionally, core TFs are selected to convert one cell type into another mostly based on literature and the experimental identification of genes that are differentially expressed in one cell type compared to the specific cell types. Currently, there is not a universal standard strategy for identifying candidate core TFs. Remarkably, several biological computational platforms are developed, which are capable of evaluating the fidelity of reprogramming methods and refining existing protocols. The current review discusses some deficiencies of reprogramming technologies in the production of a pure population of authentic target cells. Furthermore, it reviews the role of computational approaches (e.g. CellNet, KeyGenes, Mogrify, etc.) in improving (re)programming methods and consequently in regenerative medicine and cancer therapeutics.