A simple metric of promoter architecture robustly predicts expression breadth of human genes suggesting that most transcription factors are positive regulators

Sexual size dimorphism in mammals is associated with changes in the size of gene families related to brain development

In mammals, sexual size dimorphism often reflects the intensity of sexual selection, yet its connection to genomic evolution remains unexplored. Gene family size evolution can reflect shifts in the relative importance of different molecular functions. Here, we investigate the associate between brain development gene repertoire to sexual size dimorphism using 124 mammalian species. We reveal significant changes in gene family size associations with sexual size dimorphism. High levels of dimorphism correlate with an expansion of gene families enriched in olfactory sensory perception and a contraction of gene families associated with brain development functions, many of which exhibited particularly high expression in the human adult brain. These findings suggest a relationship between intense sexual selection and alterations in gene family size. These insights illustrate the complex interplay between sexual dimorphism, gene family size evolution, and their roles in mammalian brain development and function, offering a valuable understanding of mammalian genome evolution.

Selection on synonymous sites: the unwanted transcript hypothesis

Although translational selection to favour codons that match the most abundant tRNAs is not readily observed in humans, there is nonetheless selection in humans on synonymous mutations. We hypothesize that much of this synonymous site selection can be explained in terms of protection against unwanted RNAs - spurious transcripts, mis-spliced forms or RNAs derived from transposable elements or viruses. We propose not only that selection on synonymous sites functions to reduce the rate of creation of unwanted transcripts (for example, through selection on exonic splice enhancers and cryptic splice sites) but also that high-GC content (but low-CpG content), together with intron presence and position, is both particular to functional native mRNAs and used to recognize transcripts as native. In support of this hypothesis, transcription, nuclear export, liquid phase condensation and RNA degradation have all recently been shown to promote GC-rich transcripts and suppress AU/CpG-rich ones. With such 'traps' being set against AU/CpG-rich transcripts, the codon usage of native genes has, in turn, evolved to avoid such suppression. That parallel filters against AU/CpG-rich transcripts also affect the endosomal import of RNAs further supports the unwanted transcript hypothesis of synonymous site selection and explains the similar design rules that have enabled the successful use of transgenes and RNA vaccines.

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

Global patterns of enhancer activity during sea urchin embryogenesis assessed by eRNA profiling

We used capped analysis of gene expression with sequencing (CAGE-seq) to profile eRNA expression and enhancer activity during embryogenesis of a model echinoderm: the sea urchin, Strongylocentrotus purpuratus. We identified more than 18,000 enhancers that were active in mature oocytes and developing embryos and documented a burst of enhancer activation during cleavage and early blastula stages. We found that a large fraction (73.8%) of all enhancers active during the first 48 h of embryogenesis were hyperaccessible no later than the 128-cell stage and possibly even earlier. Most enhancers were located near gene bodies, and temporal patterns of eRNA expression tended to parallel those of nearby genes. Furthermore, enhancers near lineage-specific genes contained signatures of inputs from developmental gene regulatory networks deployed in those lineages. A large fraction (60%) of sea urchin enhancers previously shown to be active in transgenic reporter assays was associated with eRNA expression. Moreover, a large fraction (50%) of a representative subset of enhancers identified by eRNA profiling drove tissue-specific gene expression in isolation when tested by reporter assays. Our findings provide an atlas of developmental enhancers in a model sea urchin and support the utility of eRNA profiling as a tool for enhancer discovery and regulatory biology. The data generated in this study are available at Echinobase, the public database of information related to echinoderm genomics.

K-mer Content Changes with Node Degree in Promoter-Enhancer Network of Mouse ES Cells

Maps of Hi-C contacts between promoters and enhancers can be analyzed as networks, with cis-regulatory regions as nodes and their interactions as edges. We checked if in the published promoter-enhancer network of mouse embryonic stem (ES) cells the differences in the node type (promoter or enhancer) and the node degree (number of regions interacting with a given promoter or enhancer) are reflected by sequence composition or sequence similarity of the interacting nodes. We used counts of all k-mers (k = 4) to analyze the sequence composition and the Euclidean distance between the k-mer count vectors (k-mer distance) as the measure of sequence (dis)similarity. The results we obtained with 4-mers are interpretable in terms of dinucleotides. Promoters are GC-rich as compared to enhancers, which is known. Enhancers are enriched in scaffold/matrix attachment regions (S/MARs) patterns and depleted of CpGs. Furthermore, we show that promoters are more similar to their interacting enhancers than vice-versa. Most notably, in both promoters and enhancers, the GC content and the CpG count increase with the node degree. As a consequence, enhancers of higher node degree become more similar to promoters, whereas higher degree promoters become less similar to enhancers. We confirmed the key results also for human keratinocytes.

Emergence of co-expression in gene regulatory networks

Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.

Enhancer pleiotropy, gene expression, and the architecture of human enhancer-gene interactions

Enhancers are often studied as noncoding regulatory elements that modulate the precise spatiotemporal expression of genes in a highly tissue-specific manner. This paradigm has been challenged by recent evidence of individual enhancers acting in multiple tissues or developmental contexts. However, the frequency of these enhancers with high degrees of “pleiotropy” out of all putative enhancers is not well understood. Consequently, it is unclear how the variation of enhancer pleiotropy corresponds to the variation in expression breadth of target genes. Here, we use multi-tissue chromatin maps from diverse human tissues to investigate the enhancer–gene interaction architecture while accounting for 1) the distribution of enhancer pleiotropy, 2) the variations of regulatory links from enhancers to target genes, and 3) the expression breadth of target genes. We show that most enhancers are tissue-specific and that highly pleiotropy enhancers account for <1% of all putative regulatory sequences in the human genome. Notably, several genomic features are indicative of increasing enhancer pleiotropy, including longer sequence length, greater number of links to genes, increasing abundance and diversity of encoded transcription factor motifs, and stronger evolutionary conservation. Intriguingly, the number of enhancers per gene remains remarkably consistent for all genes (∼14). However, enhancer pleiotropy does not directly translate to the expression breadth of target genes. We further present a series of Gaussian Mixture Models to represent this organization architecture. Consequently, we demonstrate that a modest trend of more pleiotropic enhancers targeting more broadly expressed genes can generate the observed diversity of expression breadths in the human genome.

Models of the Gene Must Inform Data-Mining Strategies in Genomics

The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis.

A Contemporary Message from Mendel's Logical Empiricism

The gene is one of the most fundamental concepts in life sciences, having been developed in the mold of the Mendelian paradigm of heredity, which shaped genetics across 150 years. How could Mendel possibly be so prophetic in the middle of 19^th century, using only the small garden of the monastery as his experimental breeding field? I believe that we are indebted to Mendel's mastery of the scientific method, which was far ahead of his time. Although his experimental technology was literally garden-variety, Mendel's excellence in the method of science, algebra, and logical analysis helped him in designing the right experiment and in interpreting the results insightfully. This may be valuable to recall in today's technology-focused culture, where the center of interest tends to be on the generation and description of high-throughput datasets from specialized genomics screens. As Mendel's story suggests, progress in 21^st century genetics may also depend on the development of robust concepts and generalizations.

Retrospective analysis of model-based predictivity of human pharmacokinetics for anti-IL-36R monoclonal antibody MAB92 using a rat anti-mouse IL-36R monoclonal antibody and RNA expression data (FANTOM5)

Accurate prediction of the human pharmacokinetics (PK) of a candidate monoclonal antibody from nonclinical data is critical to maximize the success of clinical trials. However, for monoclonal antibodies exhibiting nonlinear clearance due to target-mediated drug disposition, PK predictions are particularly challenging. That challenge is further compounded for molecules lacking cross-reactivity in a nonhuman primate, in which case a surrogate antibody selective for the target in rodent may be required. For these cases, prediction of human PK must account for any interspecies differences in binding kinetics, target expression, target turnover, and potentially epitope. We present here a model-based method for predicting the human PK of MAB92 (also known as BI 655130), a humanized IgG1 κ monoclonal antibody directed against human IL-36R. Preclinical PK was generated in the mouse with a chimeric rat anti-mouse IgG2a surrogate antibody cross-reactive against mouse IL-36R. Target-specific parameters such as antibody binding affinity (K_D), internalization rate of the drug target complex (k_int), target degradation rate (k_deg), and target abundance (R₀) were integrated into the model. Two different methods of assigning human R₀ were evaluated: the first assumed comparable expression between human and mouse and the second used high-resolution mRNA transcriptome data (FANTOM5) as a surrogate for expression. Utilizing the mouse R₀ to predict human PK, AUC_0-∞ was substantially underpredicted for nonsaturating doses; however, after correcting for differences in RNA transcriptome between species, AUC_0-∞ was predicted largely within 1.5-fold of observations in first-in-human studies, demonstrating the validity of the modeling approach. Our results suggest that semi-mechanistic models incorporating RNA transcriptome data and target-specific parameters may improve the predictivity of first-in-human PK.

Gain of transcription factor binding sites is associated to changes in the expression signature of human brain and testis and is correlated to genes with higher expression breadth

The gain of transcription factor binding sites (TFBS) is believed to represent one of the major causes of biological innovation. Here we used strategies based on comparative genomics to identify 21,822 TFBS specific to the human lineage (TFBS-HS), when compared to chimpanzee and gorilla genomes. More than 40% (9,206) of these TFBS-HS are in the vicinity of 1,283 genes. A comparison of the expression pattern of these genes and the corresponding orthologs in chimpanzee and gorilla identified genes differentially expressed in human tissues. These genes show a more divergent expression pattern in the human testis and brain, suggesting a role for positive selection in the fixation of TFBS gains. Genes associated with TFBS-HS were enriched in gene ontology categories related to transcriptional regulation, signaling, differentiation/development and nervous system. Furthermore, genes associated with TFBS-HS present a higher expression breadth when compared to genes in general. This biased distribution is due to a preferential gain of TFBS in genes with higher expression breadth rather than a shift in the expression pattern after the gain of TFBS.

Modelling of the breadth of expression from promoter architectures identifies pro-housekeeping transcription factors

Understanding how regulatory elements control mammalian gene expression is a challenge of post-genomic era. We previously reported that size of proximal promoter architecture predicted the breadth of expression (fraction of tissues in which a gene is expressed). Herein, the contributions of individual transcription factors (TFs) were quantified. Several technologies of statistical modelling were utilized and compared: tree models, generalized linear models (GLMs, without and with regularization), Bayesian GLMs and random forest. Both linear and non-linear modelling strategies were explored. Encouragingly, different models led to similar statistical conclusions and biological interpretations. The majority of ENCODE TFs correlated positively with housekeeping expression, a minority correlated negatively. Thus, housekeeping expression can be understood as a cumulative effect of many types of TF binding sites. This is accompanied by the exclusion of fewer types of binding sites for TFs which are repressors, or support cell lineage commitment or temporarily inducible or spatially-restricted expression.

1700108J01Rik and 1700101O22Rik are mouse testis-specific long non-coding RNAs

Long non-coding RNAs (lncRNAs; > 200 nucleotides in length) have attracted attention as fine-tuners of gene expression. However, little is known about the cell- and stage-specific expression pattern and function of lncRNAs in spermatogenesis. The purpose of this study was to identify mouse testis-associated lncRNAs using a combination of computational and experimental approaches. We first used the FANTOM5 database to survey lncRNA expression in the mouse testis and performed reverse transcription quantitative polymerase chain reaction (real-time PCR) and in situ hybridization (ISH) analyses. In silico analysis showed that most of the highly expressed lncRNAs in the adult mouse testis were testis-specific lncRNAs and were expressed at and following the initiation of spermatogenesis. We selected the antisense lncRNA 1700108J01Rik and long intergenic non-coding RNA 1700101O22Rik from the most highly expressed lncRNAs in the adult testis for further analysis. Real-time PCR analysis confirmed that 1700108J01Rik and 1700101O22Rik were specifically expressed in the testis. ISH analysis revealed that the two mouse-testis-specific lncRNAs were expressed exclusively in testicular germ cells in meiotic prophase and the round spermatid stage, which coincide with the period of transcriptional reactivation during spermatogenesis. The cytoplasmic distribution of these lncRNAs revealed by ISH suggests their involvement in post-transcriptional gene regulation rather than in epigenetic or transcriptional regulation. Our data provide new insight into testis-associated lncRNAs that will be useful in expression and functional studies of spermatogenesis.

Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?

We review computational predictions of expression from the promoter architecture - the set of transcription factors that can bind the proximal promoter. We focus on spatial expression patterns in animals with complex body plans and many distinct tissue types. This field is ripe for change as functional genomics datasets accumulate for both expression and protein-DNA interactions. While there has been some success in predicting the breadth of expression (i.e., the fraction of tissue types a gene is expressed in), predicting tissue specificity remains challenging. We discuss how progress can be achieved through either machine learning or complementary combinatorial data mining. The likely impact of single-cell expression data is considered. Finally, we discuss the design of artificial promoters as a practical application.

Integrative Genomics Outlines a Biphasic Glucose Response and a ChREBP-RORγ Axis Regulating Proliferation in β Cells

Glucose is an important inducer of insulin secretion, but it also stimulates long-term adaptive changes in gene expression that can either promote or antagonize the proliferative potential and function of β cells. Here, we have generated time-resolved profiles of enhancer and transcriptional activity in response to glucose in the INS-1E pancreatic β cell line. Our data outline a biphasic response with a first transcriptional wave during which metabolic genes are activated, and a second wave where cell-cycle genes are activated and β cell identity genes are repressed. The glucose-sensing transcription factor ChREBP directly activates first wave enhancers, whereas repression and activation of second wave enhancers are indirect. By integrating motif enrichment within late-regulated enhancers with expression profiles of the associated transcription factors, we have identified multiple putative regulators of the second wave. These include RORγ, the activity of which is important for glucose-induced proliferation of both INS-1E and primary rat β cells.

Identifying cell-specific microRNA transcriptional start sites

MOTIVATION

Identification of microRNA (miRNA) transcriptional start sites (TSSs) is crucial to understand the transcriptional regulation of miRNA. As miRNA expression is highly cell specific, an automatic and systematic method that could identify miRNA TSSs accurately and cell specifically is in urgent requirement.

RESULTS

A workflow to identify the TSSs of miRNAs was built by integrating the data of H3K4me3 and DNase I hypersensitive sites as well as combining the conservation level and sequence feature. By applying the workflow to the data for 54 cell lines from the ENCODE project, we successfully identified TSSs for 663 intragenic miRNAs and 620 intergenic miRNAs, which cover 84.2% (1283/1523) of all miRNAs recorded in miRBase 18. For these cell lines, we found 4042 alternative TSSs for intragenic miRNAs and 3186 alternative TSSs for intergenic miRNAs. Our method achieved a better performance than the previous non-cell-specific methods on miRNA TSSs. The cell-specific method developed by Georgakilas et al. gives 158 TSSs of higher accuracy in two cell lines, benefitting from the employment of deep-sequencing technique. In contrast, our method provided a much higher number of miRNA TSSs (7228) for a broader range of cell lines without the limitation of costly deep-sequencing data, thus being more applicable for various experimental cases. Analysis showed that upstream promoters at - 2 kb to - 200 bp of TSS are more conserved for independently transcribed miRNAs, while for miRNAs transcribed with host genes, their core promoters (-200 bp to 200 bp of TSS) are significantly conserved.

AVAILABILITY AND IMPLEMENTATION

Predicted miRNA TSSs and promoters can be downloaded from supplementary files.

CONTACT

jwang@nju.edu.cn or jlee@nju.edu.cn or edgar.wingender@bioinf.med.uni-goettingen.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X’s gene content, gene expression, and evolution.

Laurence Hurst, Lukasz Huminiecki, and the FANTOM5 consortium propose a new explanation for the peculiar expression properties of genes on the human X chromosome, based on the premise that very high expression levels cannot be achieved on a haploid-expressed chromosome.

Genes located on the human X chromosome are not a random mix of genes: they tend to be expressed in relatively few tissues or are specific for a particular set of tissues, e.g., brain regions. Prior attempts to explain this skewed gene content have hypothesized that the X chromosome might be peculiar because it has to balance mutations that are advantageous to one sex but deleterious to the other, or because it has to shut down during the process of sperm manufacture in males. Here we suggest and test a third possible explanation: that genes on the X chromosome are limited in their transcription levels and thus tend to be genes that are lowly or specifically expressed. We consider the suggestion that since these genes can only be expressed from one chromosome, as males only have one X, the ability to express a gene at very high rates is limited owing to potential transcriptional traffic jams. As predicted, we find that human X-located genes have maximal expression rates far below that of genes residing on autosomes. When we look at genes that have moved onto or off the X chromosome during recent evolution, we find the maximal expression is higher when not on the X chromosome. We also find that X-located genes that are relatively highly expressed are not able to increase their expression level further. Our model explains both the enrichment for tissue specificity and the paucity of certain tissues with X-located genes. Genes underrepresented on the X are either expressed in many tissues—such genes tend to have high maximal expression—or are from tissues that require a lot of transcription (e.g., fast secreting tissues like the liver). Just as many of the findings cannot be explained by the two earlier models, neither can the traffic jam model explain all the peculiar features of the genes found on the X chromosome. Indeed, we find evidence of a reproduction-related bias in X-located genes, even after allowing for the traffic jam problem.

Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line

Mammals are composed of hundreds of different cell types with specialized functions. Each of these cellular phenotypes are controlled by different combinations of transcription factors. Using a human non islet cell insulinoma cell line (TC-YIK) which expresses insulin and the majority of known pancreatic beta cell specific genes as an example, we describe a general approach to identify key cell-type-specific transcription factors (TFs) and their direct and indirect targets. By ranking all human TFs by their level of enriched expression in TC-YIK relative to a broad collection of samples (FANTOM5), we confirmed known key regulators of pancreatic function and development. Systematic siRNA mediated perturbation of these TFs followed by qRT-PCR revealed their interconnections with NEUROD1 at the top of the regulation hierarchy and its depletion drastically reducing insulin levels. For 15 of the TF knock-downs (KD), we then used Cap Analysis of Gene Expression (CAGE) to identify thousands of their targets genome-wide (KD-CAGE). The data confirm NEUROD1 as a key positive regulator in the transcriptional regulatory network (TRN), and ISL1, and PROX1 as antagonists. As a complimentary approach we used ChIP-seq on four of these factors to identify NEUROD1, LMX1A, PAX6, and RFX6 binding sites in the human genome. Examining the overlap between genes perturbed in the KD-CAGE experiments and genes with a ChIP-seq peak within 50 kb of their promoter, we identified direct transcriptional targets of these TFs. Integration of KD-CAGE and ChIP-seq data shows that both NEUROD1 and LMX1A work as the main transcriptional activators. In the core TRN (i.e., TF-TF only), NEUROD1 directly transcriptionally activates the pancreatic TFs HSF4, INSM1, MLXIPL, MYT1, NKX6-3, ONECUT2, PAX4, PROX1, RFX6, ST18, DACH1, and SHOX2, while LMX1A directly transcriptionally activates DACH1, SHOX2, PAX6, and PDX1. Analysis of these complementary datasets suggests the need for caution in interpreting ChIP-seq datasets. (1) A large fraction of binding sites are at distal enhancer sites and cannot be directly associated to their targets, without chromatin conformation data. (2) Many peaks may be non-functional: even when there is a peak at a promoter, the expression of the gene may not be affected in the matching perturbation experiment.

Modulating the Genomic Programming of Adipocytes

The ability to modify the transcriptional program in response to external signals provides a way for mammalian cells to alter their biological fate and properties, thereby adapting to changes in the environment. Adipocytes are excellent examples of differentiated cells that possess a striking transcriptional plasticity when exposed to physiological and metabolic stimuli. In our work, we have focused on understanding the processes responsible for modulating the genomic programming in response to different external signals. Thus, we have shown that browning of human adipocytes with rosiglitazone, an antidiabetic agonist of the key adipocyte transcription factor peroxisome proliferator-activated receptor γ (PPARγ), involves redistribution of PPARγ binding to form browning-selective PPARγ super-enhancers that drive expression of key browning genes. These include genes encoding transcriptional regulators, such as Krüppel-like factor 11 (KLF11) that are essential for modulating the genomic program in white adipocytes to induce browning. Furthermore, we have shown that acute suppression of adipocyte genes by the proinflammatory cytokine, tumor necrosis factor (TNF), involves redistribution of cofactors to enhancers activated by the master inflammatory regulator, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). Interestingly, this redistribution occurs selectively from enhancers with high-cofactor occupancies, thereby predominantly affecting super-enhancers and their associated genes. We propose that this is a general mechanism contributing to transcriptional repression associated with activation of signal-dependent transcription factors.

Paradigm shifts in genomics through the FANTOM projects

Big leaps in science happen when scientists from different backgrounds interact. In the past 15 years, the FANTOM Consortium has brought together scientists from different fields to analyze and interpret genomic data produced with novel technologies, including mouse full-length cDNAs and, more recently, expression profiling at single-nucleotide resolution by cap-analysis gene expression. The FANTOM Consortium has provided the most comprehensive mouse cDNA collection for functional studies and extensive maps of the human and mouse transcriptome comprising promoters, enhancers, as well as the network of their regulatory interactions. More importantly, serendipitous observations of the FANTOM dataset led us to realize that the mammalian genome is pervasively transcribed, even from retrotransposon elements, which were previously considered junk DNA. The majority of products from the mammalian genome are long non-coding RNAs (lncRNAs), including sense-antisense, intergenic, and enhancer RNAs. While the biological function has been elucidated for some lncRNAs, more than 98 % of them remain without a known function. We argue that large-scale studies are urgently needed to address the functional role of lncRNAs.

Populational landscape of INDELs affecting transcription factor-binding sites in humans

BACKGROUND

Differences in gene expression have a significant role in the diversity of phenotypes in humans. Here we integrated human public data from ENCODE, 1000 Genomes and Geuvadis to explore the populational landscape of INDELs affecting transcription factor-binding sites (TFBS). A significant fraction of TFBS close to the transcription start site of known genes is affected by INDELs with a consequent effect at the expression of the associated gene.

RESULTS

Hundreds of TFBS-affecting INDELs (TFBS-ID) show a differential frequency between human populations, suggesting a role of natural selection in the spread of such variant INDELs. A comparison with a dataset of known human genomic regions under natural selection allowed us to identify several cases of TFBS-ID likely involved in populational adaptations. Ontology analyses on the differential TFBS-ID further indicated several biological processes under natural selection in different populations.

CONCLUSION

Together, our results strongly suggest that INDELs have an important role in modulating gene expression patterns in humans. The dataset we make available, together with other data reporting variability at both regulatory and coding regions of genes, represent a powerful tool for studies aiming to better understand the evolution of gene regulatory networks in humans.