MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences

The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes

BACKGROUND

The precise prediction of transcription factor binding sites (TFBSs) is pivotal for unraveling the gene regulatory networks underlying biological processes. While numerous tools have emerged for in silico TFBS prediction in recent years, the evolving landscape of computational biology necessitates thorough assessments of tool performance to ensure accuracy and reliability. Only a limited number of studies have been conducted to evaluate the performance of TFBS prediction tools comprehensively. Thus, the present study focused on assessing twelve widely used TFBS prediction tools and four de novo motif discovery tools using a benchmark dataset comprising real, generic, Markov, and negative sequences. TFBSs of Arabidopsis thaliana and Homo sapiens genomes downloaded from the JASPAR database were implanted in these sequences and the performance of tools was evaluated using several statistical parameters at different overlap percentages between the lengths of known and predicted binding sites.

RESULTS

Overall, the Multiple Cluster Alignment and Search Tool (MCAST) emerged as the best TFBS prediction tool, followed by Find Individual Motif Occurrences (FIMO) and MOtif Occurrence Detection Suite (MOODS). In addition, MotEvo and Dinucleotide Weight Tensor Toolbox (DWT-toolbox) demonstrated the highest sensitivity in identifying TFBSs at 90% and 80% overlap. Further, MCAST and DWT-toolbox managed to demonstrate the highest sensitivity across all three data types real, generic, and Markov. Among the de novo motif discovery tools, the Multiple Em for Motif Elicitation (MEME) emerged as the best performer. An analysis of the promoter regions of genes involved in the anthocyanin biosynthesis pathway in plants and the pentose phosphate pathway in humans, using the three best-performing tools, revealed considerable variation among the top 20 motifs identified by these tools.

CONCLUSION

The findings of this study lay a robust groundwork for selecting optimal TFBS prediction tools for future research. Given the variability observed in tool performance, employing multiple tools for identifying TFBSs in a set of sequences is highly recommended. In addition, further studies are recommended to develop an integrated toolbox that incorporates TFBS prediction or motif discovery tools, aiming to streamline result precision and accuracy.

On the importance of sequence alignment inspections in plastid phylogenomics - an example from revisiting the relationships of the water-lilies

The water-lily clade represents the second earliest-diverging branch of angiosperms. Most of its species belong to Nymphaeaceae, of which the "core Nymphaeaceae"-comprising the genera Euryale, Nymphaea and Victoria-is the most diverse clade. Despite previous molecular phylogenetic studies on the core Nymphaeaceae, various aspects of their evolutionary relationships have remained unresolved. The length-variable introns and intergenic spacers are known to contain most of the sequence variability within the water-lily plastomes. Despite the challenges with multiple sequence alignment, any new molecular phylogenetic investigation on the core Nymphaeaceae should focus on these noncoding plastome regions. For example, a new plastid phylogenomic study on the core Nymphaeaceae should generate DNA sequence alignments of all plastid introns and intergenic spacers based on the principle of conserved sequence motifs. In this investigation, we revisit the phylogenetic history of the core Nymphaeaceae by employing such an approach. Specifically, we use a plastid phylogenomic analysis strategy in which all coding and noncoding partitions are separated and then undergo software-driven DNA sequence alignment, followed by a motif-based alignment inspection and adjustment. This approach allows us to increase the reliability of the character base compared to the default practice of aligning complete plastomes through software algorithms alone. Our approach produces significantly different phylogenetic tree reconstructions for several of the plastome regions under study. The results of these reconstructions underscore that Nymphaea is paraphyletic in its current circumscription, that each of the five subgenera of Nymphaea is monophyletic, and that the subgenus Nymphaea is sister to all other subgenera of Nymphaea. Our results also clarify many evolutionary relationships within the Nymphaea subgenera Brachyceras, Hydrocallis and Nymphaea. In closing, we discuss whether the phylogenetic reconstructions obtained through our motif-based alignment adjustments are in line with morphological evidence on water-lily evolution.

Epigenetic silencing of ZIC4 unveils a potential tumor suppressor role in pediatric choroid plexus carcinoma

Zic family member ZIC4 is a transcription factor that has been shown to be silenced in several cancers. However, understanding the regulation and function of ZIC4 in pediatric choroid plexus tumors (CPTs) remained limited. This study employed data mining and bioinformatics analysis to investigate the DNA methylation status of ZIC4 in CPTs and its correlation with patient survival. Our results unveiled ZIC4 methylation as a segregating factor, dividing CPT cohorts into two clusters, with hyper-methylation linked to adverse prognosis. Hyper-methylation of ZIC4 was confirmed in a choroid plexus carcinoma-derived cell line (CCHE-45) by bisulfite sequencing. Furthermore, our study demonstrated that demethylating agent and a histone methyltransferase inhibitor could reverse ZIC4 silencing. RNA sequencing and proteomic analysis showed that ZIC4 over-expression influenced genes and proteins involved in immune response, antigen processing and presentation, endoplasmic reticulum stress, and metabolism. Functionally, re-expressing ZIC4 negatively impacted cell proliferation and migration. Ultimately, these findings underscore ZIC4 hyper-methylation as a prognostic marker in CPTs and shed light on potential mechanisms underlying its tumor suppressor role in CPC. This insight paves the way for novel therapeutic targets in treating aggressive CPTs.

Vitamin D Receptor Regulates the Expression of the Grainyhead-Like 1 Gene

Vitamin D plays an important pleiotropic role in maintaining global homeostasis of the human body. Its functions go far beyond skeletal health, playing a crucial role in a plethora of cellular functions, as well as in extraskeletal health, ensuring the proper functioning of multiple human organs, including the skin. Genes from the Grainyhead-like (GRHL) family code for transcription factors necessary for the development and maintenance of various epithelia. Even though they are involved in many processes regulated by vitamin D, a direct link between vitamin D-mediated cellular pathways and GRHL genes has never been described. We employed various bioinformatic methods, quantitative real-time PCR, chromatin immunoprecipitation, reporter gene assays, and calcitriol treatments to investigate this issue. We report that the vitamin D receptor (VDR) binds to a regulatory region of the Grainyhead-like 1 (GRHL1) gene and regulates its expression. Ectopic expression of VDR and treatment with calcitriol alters the expression of the GRHL1 gene. The evidence presented here indicates a role of VDR in the regulation of expression of GRHL1 and correspondingly a role of GRHL1 in mediating the actions of vitamin D.

Characterization of cell-fate decision landscapes by estimating transcription factor dynamics

Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using in silico data and application to pancreatic endocrine cell differentiation datasets highlight both known and potentially novel lineage-specific regulators. Notably, we uncovered undescribed transcription factors of an enterochromaffin-like population during in vitro differentiation toward ß-like cells. FateCompass provides a valuable framework for hypothesis generation, advancing our understanding of the gene regulatory networks driving cell-fate decisions.

Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

Single-cell sortChIC identifies hierarchical chromatin dynamics during hematopoiesis

Post-translational histone modifications modulate chromatin activity to affect gene expression. How chromatin states underlie lineage choice in single cells is relatively unexplored. We develop sort-assisted single-cell chromatin immunocleavage (sortChIC) and map active (H3K4me1 and H3K4me3) and repressive (H3K27me3 and H3K9me3) histone modifications in the mouse bone marrow. During differentiation, hematopoietic stem and progenitor cells (HSPCs) acquire active chromatin states mediated by cell-type-specifying transcription factors, which are unique for each lineage. By contrast, most alterations in repressive marks during differentiation occur independent of the final cell type. Chromatin trajectory analysis shows that lineage choice at the chromatin level occurs at the progenitor stage. Joint profiling of H3K4me1 and H3K9me3 demonstrates that cell types within the myeloid lineage have distinct active chromatin but share similar myeloid-specific heterochromatin states. This implies a hierarchical regulation of chromatin during hematopoiesis: heterochromatin dynamics distinguish differentiation trajectories and lineages, while euchromatin dynamics reflect cell types within lineages.

Antisense-oligonucleotide-mediated perturbation of long non-coding RNA reveals functional features in stem cells and across cell types

Within the scope of the FANTOM6 consortium, we perform a large-scale knockdown of 200 long non-coding RNAs (lncRNAs) in human induced pluripotent stem cells (iPSCs) and systematically characterize their roles in self-renewal and pluripotency. We find 36 lncRNAs (18%) exhibiting cell growth inhibition. From the knockdown of 123 lncRNAs with transcriptome profiling, 36 lncRNAs (29.3%) show molecular phenotypes. Integrating the molecular phenotypes with chromatin-interaction assays further reveals cis- and trans-interacting partners as potential primary targets. Additionally, cell-type enrichment analysis identifies lncRNAs associated with pluripotency, while the knockdown of LINC02595, CATG00000090305.1, and RP11-148B6.2 modulates colony formation of iPSCs. We compare our results with previously published fibroblasts phenotyping data and find that 2.9% of the lncRNAs exhibit a consistent cell growth phenotype, whereas we observe 58.3% agreement in molecular phenotypes. This highlights that molecular phenotyping is more comprehensive in revealing affected pathways.

Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands

BACKGROUND

Emerging infectious disease-causing RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it is important to characterize the bat genome from multiple perspectives. Unsupervised machine learning methods for extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights. We previously established a batch-learning self-organizing map (BLSOM) of the oligonucleotide composition that reveals novel genome characteristics from big sequence data.

RESULTS

In this study, using the oligonucleotide BLSOM, we conducted a comparative genomic study of humans and six bat species. BLSOM is an explainable-type machine learning algorithm that reveals the diagnostic oligonucleotides contributing to sequence clustering (self-organization). When unsupervised machine learning reveals unexpected and/or characteristic features, these features can be studied in more detail via the much simpler and more direct standard distribution map method. Based on this combined strategy, we identified the Mb-level enrichment of CG dinucleotide (Mb-level CpG islands) around the termini of bat long-scaffold sequences. In addition, a class of CG-containing oligonucleotides were enriched in the centromeric and pericentromeric regions of human chromosomes. Oligonucleotides longer than tetranucleotides often represent binding motifs for a wide variety of proteins (e.g., transcription factor binding sequences (TFBSs)). By analyzing the penta- and hexanucleotide composition, we observed the evident enrichment of a wide range of hexanucleotide TFBSs in centromeric and pericentromeric heterochromatin regions on all human chromosomes.

CONCLUSION

Function of transcription factors (TFs) beyond their known regulation of gene expression (e.g., TF-mediated looping interactions between two different genomic regions) has received wide attention. The Mb-level TFBS and CpG islands are thought to be involved in the large-scale nuclear organization, such as centromere and telomere clustering. TFBSs, which are enriched in centromeric and pericentromeric heterochromatin regions, are thought to play an important role in the formation of nuclear 3D structures. Our machine learning-based analysis will help us to understand the differential features of nuclear 3D structures in the human and bat genomes.

Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements

Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center ( https://danio-code.zfin.org ) combines a total of 1,802 sets of unpublished and re-analyzed published genomic data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals.

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts.

Single-cell RNA-sequencing technology gives access to cell cycle dynamics without externally perturbing the cell. Here the authors present DeepCycle,a robust deep learning method to infer the cell cycle state in single cells from scRNA-seq data.

circKCNN2 suppresses the recurrence of hepatocellular carcinoma at least partially via regulating miR-520c-3p/methyl-DNA-binding domain protein 2 axis

BACKGROUND

Recurrence is the major cause of hepatocellular carcinoma (HCC) death. We aimed to identify circular RNA (circRNA) with predictive and therapeutic value for recurrent HCC.

METHODS

Tissue samples from recurrent and non-recurrent HCC patients were subjected to circRNA sequencing and transcriptome sequencing. circKCNN2 was identified through multi-omics analyses. The effects of circKCNN2 on HCC were evaluated in cells, animals, database of The Cancer Genome Atlas, and a cohort with 130 HCC patients. circRNA precipitation, chromatin immunoprecipitation assay, RNA pull-down, luciferase assay, and cell experiments were applied to evaluate the interaction of circKCNN2 with miRNAs and proteins. The association between circKCNN2 and the therapeutic effect of lenvatinib was investigated in HCC cell lines and HCC tissue-derived organoids.

RESULTS

The expression of circKCNN2 was downregulated in HCC tissues and predicted a favorable overall survival and recurrence-free survival. The expression of circKCNN2 was positively correlated with the parental gene, potassium calcium-activated channel subfamily N member (KCNN2). Nuclear transcription factor Y subunit alpha (NFYA) was proven to inhibit the promoter activity of KCNN2, downregulate the expression of KCNN2 and circKCNN2, and predict an unfavorable recurrence-free survival. Ectopic expression of circKCNN2 inhibited HCC cell proliferation, colony formation, migration, and tumor formation in a mouse model. miR-520c-3p sponged by circKCNN2 could reverse the inhibitory effect of circKCNN2 on HCC cells and down-regulate the expression of methyl-DNA-binding domain protein 2 (MBD2). The intratumoral expression of MBD2 predicted a favorable recurrence-free survival. circKCNN2 down-regulated the expression of fibroblast growth factor receptor 4 (FGFR4), which can be reversed by miR-520c-3p and knockdown of MBD2. Lenvatinib inhibited the expression of FGFR4 and upregulated the expression of circKCNN2 and MBD2. Ectopic expression of circKCNN2 in HCC cells enhanced the therapeutic effect of lenvatinib. However, the high inherent level of circKCNN2 in HCC cells was associated with lenvatinib resistance.

CONCLUSIONS

circKCNN2, transcriptionally repressed by NFYA, suppresses HCC recurrence via the miR-520c-3p/MBD2 axis. Inherent level of circKCNN2 in HCC cells predisposes anti-tumor effect of lenvatinib possibly because both circKCNN2 and lenvatinib repress the expression of FGFR4. circKCNN2 may be a promising predictive biomarker and therapeutic agent for HCC recurrence.

Harnessing natural variation to identify cis regulators of sex-biased gene expression in a multi-strain mouse liver model

Sex differences in gene expression are widespread in the liver, where many autosomal factors act in tandem with growth hormone signaling to regulate individual variability of sex differences in liver metabolism and disease. Here, we compare hepatic transcriptomic and epigenetic profiles of mouse strains C57BL/6J and CAST/EiJ, representing two subspecies separated by 0.5-1 million years of evolution, to elucidate the actions of genetic factors regulating liver sex differences. We identify 144 protein coding genes and 78 lncRNAs showing strain-conserved sex bias; many have gene ontologies relevant to liver function, are more highly liver-specific and show greater sex bias, and are more proximally regulated than genes whose sex bias is strain-dependent. The strain-conserved genes include key growth hormone-dependent transcriptional regulators of liver sex bias; however, three other transcription factors, Trim24, Tox, and Zfp809, lose their sex-biased expression in CAST/EiJ mouse liver. To elucidate the observed strain specificities in expression, we characterized the strain-dependence of sex-biased chromatin opening and enhancer marks at cis regulatory elements (CREs) within expression quantitative trait loci (eQTL) regulating liver sex-biased genes. Strikingly, 208 of 286 eQTLs with strain-specific, sex-differential effects on expression were associated with a complete gain, loss, or reversal of the sex differences in expression between strains. Moreover, 166 of the 286 eQTLs were linked to the strain-dependent gain or loss of localized sex-biased CREs. Remarkably, a subset of these CREs apparently lacked strain-specific genetic variants yet showed coordinated, strain-dependent sex-biased epigenetic regulation. Thus, we directly link hundreds of strain-specific genetic variants to the high variability in CRE activity and expression of sex-biased genes and uncover underlying genetically-determined epigenetic states controlling liver sex bias in genetically diverse mouse populations.

The Jun-dependent axon regeneration gene program: Jun promotes regeneration over plasticity

The regeneration-associated gene (RAG) expression program is activated in injured peripheral neurons after axotomy and enables long-distance axon re-growth. Over 1000 genes are regulated, and many transcription factors are upregulated or activated as part of this response. However, a detailed picture of how RAG expression is regulated is lacking. In particular, the transcriptional targets and specific functions of the various transcription factors are unclear. Jun was the first-regeneration-associated transcription factor identified and the first shown to be functionally important. Here we fully define the role of Jun in the RAG expression program in regenerating facial motor neurons. At 1, 4 and 14 days after axotomy, Jun upregulates 11, 23 and 44% of the RAG program, respectively. Jun functions relevant to regeneration include cytoskeleton production, metabolic functions and cell activation, and the downregulation of neurotransmission machinery. In silico analysis of promoter regions of Jun targets identifies stronger over-representation of AP1-like sites than CRE-like sites, although CRE sites were also over-represented in regions flanking AP1 sites. Strikingly, in motor neurons lacking Jun, an alternative SRF-dependent gene expression program is initiated after axotomy. The promoters of these newly expressed genes exhibit over-representation of CRE sites in regions near to SRF target sites. This alternative gene expression program includes plasticity-associated transcription factors and leads to an aberrant early increase in synapse density on motor neurons. Jun thus has the important function in the early phase after axotomy of pushing the injured neuron away from a plasticity response and towards a regenerative phenotype.

Regulation of transcription reactivation dynamics exiting mitosis

Proliferating cells experience a global reduction of transcription during mitosis, yet their cell identity is maintained and regulatory information is propagated from mother to daughter cells. Mitotic bookmarking by transcription factors has been proposed as a potential mechanism to ensure the reactivation of transcription at the proper set of genes exiting mitosis. Recently, mitotic transcription and waves of transcription reactivation have been observed in synchronized populations of human hepatoma cells. However, the study did not consider that mitotic-arrested cell populations progressively desynchronize leading to measurements of gene expression on a mixture of cells at different internal cell-cycle times. Moreover, it is not well understood yet what is the precise role of mitotic bookmarking on mitotic transcription as well as on the transcription reactivation waves. Ultimately, the core gene regulatory network driving the precise transcription reactivation dynamics remains to be identified. To address these questions, we developed a mathematical model to correct for the progressive desynchronization of cells and estimate gene expression dynamics with respect to a cell-cycle pseudotime. Furthermore, we used a multiple linear regression model to infer transcription factor activity dynamics. Our analysis allows us to characterize waves of transcription factor activities exiting mitosis and predict a core gene regulatory network responsible of the transcription reactivation dynamics. Moreover, we identified more than 60 transcription factors that are highly active during mitosis and represent new candidates of mitotic bookmarking factors which could be relevant therapeutic targets to control cell proliferation.

Specific gene expression patterns confer particular identities to cells. During proliferation, cells undergo mitosis when chromosomes are formed and segregated into two new cells leading to a global downregulation of gene expression. Yet, cell identity is propagated from mother to daughter cells by the reactivation of gene expression at the appropriate set of genes once mitosis is completed. Mitotic bookmarking has been proposed as a mechanism to regulate this process. Indeed certain regulatory factors tag genes during mitosis to promote gene reactivation in the next cycle. Here we analyze gene expression over time measured on synchronized cell populations by using a new generation sequencing technique. To do so, we proposed a mathematical model to obtain the exact gene expression dynamics with respect to the cell-cycle progression and identified waves of genes reactivation during mitosis and the transition to the next cycle. Also, we developed a computational method that allowed us to predict key regulatory factors that drive this process and predict new candidates that could be involved in mitotic bookmarking. These regulatory factors could be relevant therapeutic targets to control cell proliferation.

Transcription factors Krüppel-like factor 4 and paired box 5 regulate the expression of the Grainyhead-like genes

Genes from the Grainyhead-like (GRHL) family code for transcription factors necessary for the development and maintenance of various epithelia. These genes are also very important in the development of many types of cancer. However, little is known about the regulation of expression of GRHL genes. Previously, there were no systematic analyses of the promoters of GRHL genes or transcription factors that bind to these promoters. Here we report that the Krüppel-like factor 4 (KLF4) and the paired box 5 factor (PAX5) bind to the regulatory regions of the GRHL genes and regulate their expression. Ectopic expression of KLF4 or PAX5 alters the expression of GRHL genes. In KLF4-overexpressing HEK293 cells, the expression of GRHL1 and GRHL3 genes was upregulated by 32% and 60%, respectively, whereas the mRNA level of GRHL2 gene was lowered by 28% when compared to the respective controls. The levels of GRHL1 and GRHL3 expression were decreased by 30% or 33% in PAX5-overexpressing HEK293 cells. The presence of minor frequency allele of single nucleotide polymorphism rs115898376 in the promoter of the GRHL1 gene affected the binding of KLF4 to this site. The evidence presented here suggests an important role of KLF4 and PAX5 in the regulation of expression of GRHL1-3 genes.

A flexible repertoire of transcription factor binding sites and a diversity threshold determines enhancer activity in embryonic stem cells

Transcriptional enhancers are critical for development and phenotype evolution and are often mutated in disease contexts; however, even in well-studied cell types, the sequence code conferring enhancer activity remains unknown. To examine the enhancer regulatory code for pluripotent stem cells, we identified genomic regions with conserved binding of multiple transcription factors in mouse and human embryonic stem cells (ESCs). Examination of these regions revealed that they contain on average 12.6 conserved transcription factor binding site (TFBS) sequences. Enriched TFBSs are a diverse repertoire of 70 different sequences representing the binding sequences of both known and novel ESC regulators. Using a diverse set of TFBSs from this repertoire was sufficient to construct short synthetic enhancers with activity comparable to native enhancers. Site-directed mutagenesis of conserved TFBSs in endogenous enhancers or TFBS deletion from synthetic sequences revealed a requirement for 10 or more different TFBSs. Furthermore, specific TFBSs, including the POU5F1:SOX2 comotif, are dispensable, despite cobinding the POU5F1 (also known as OCT4), SOX2, and NANOG master regulators of pluripotency. These findings reveal that a TFBS sequence diversity threshold overrides the need for optimized regulatory grammar and individual TFBSs that recruit specific master regulators.

FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs

The Functional ANnoTation Of the Mammalian genome (FANTOM) Consortium has continued to provide extensive resources in the pursuit of understanding the transcriptome, and transcriptional regulation, of mammalian genomes for the last 20 years. To share these resources with the research community, the FANTOM web-interfaces and databases are being regularly updated, enhanced and expanded with new data types. In recent years, the FANTOM Consortium's efforts have been mainly focused on creating new non-coding RNA datasets and resources. The existing FANTOM5 human and mouse miRNA atlas was supplemented with rat, dog, and chicken datasets. The sixth (latest) edition of the FANTOM project was launched to assess the function of human long non-coding RNAs (lncRNAs). From its creation until 2020, FANTOM6 has contributed to the research community a large dataset generated from the knock-down of 285 lncRNAs in human dermal fibroblasts; this is followed with extensive expression profiling and cellular phenotyping. Other updates to the FANTOM resource includes the reprocessing of the miRNA and promoter atlases of human, mouse and chicken with the latest reference genome assemblies. To facilitate the use and accessibility of all above resources we further enhanced FANTOM data viewers and web interfaces. The updated FANTOM web resource is publicly available at https://fantom.gsc.riken.jp/.

Mitochondrial gene signature in the prefrontal cortex for differential susceptibility to chronic stress

Mitochondrial dysfunction was highlighted as a crucial vulnerability factor for the development of depression. However, systemic studies assessing stress-induced changes in mitochondria-associated genes in brain regions relevant to depression symptomatology remain scarce. Here, we performed a genome-wide transcriptomic study to examine mitochondrial gene expression in the prefrontal cortex (PFC) and nucleus accumbens (NAc) of mice exposed to multimodal chronic restraint stress. We identified mitochondria-associated gene pathways as most prominently affected in the PFC and with lesser significance in the NAc. A more detailed mitochondrial gene expression analysis revealed that in particular mitochondrial DNA-encoded subunits of the oxidative phosphorylation complexes were altered in the PFC. The comparison of our data with a reanalyzed transcriptome data set of chronic variable stress mice and major depression disorder subjects showed that the changes in mitochondrial DNA-encoded genes are a feature generalizing to other chronic stress-protocols as well and might have translational relevance. Finally, we provide evidence for changes in mitochondrial outputs in the PFC following chronic stress that are indicative of mitochondrial dysfunction. Collectively, our work reinforces the idea that changes in mitochondrial gene expression are key players in the prefrontal adaptations observed in individuals with high behavioral susceptibility and resilience to chronic stress.

Comparative transcriptomics of primary cells in vertebrates

Gene expression profiles in homologous tissues have been observed to be different between species, which may be due to differences between species in the gene expression program in each cell type, but may also reflect differences in cell type composition of each tissue in different species. Here, we compare expression profiles in matching primary cells in human, mouse, rat, dog, and chicken using Cap Analysis Gene Expression (CAGE) and short RNA (sRNA) sequencing data from FANTOM5. While we find that expression profiles of orthologous genes in different species are highly correlated across cell types, in each cell type many genes were differentially expressed between species. Expression of genes with products involved in transcription, RNA processing, and transcriptional regulation was more likely to be conserved, while expression of genes encoding proteins involved in intercellular communication was more likely to have diverged during evolution. Conservation of expression correlated positively with the evolutionary age of genes, suggesting that divergence in expression levels of genes critical for cell function was restricted during evolution. Motif activity analysis showed that both promoters and enhancers are activated by the same transcription factors in different species. An analysis of expression levels of mature miRNAs and of primary miRNAs identified by CAGE revealed that evolutionary old miRNAs are more likely to have conserved expression patterns than young miRNAs. We conclude that key aspects of the regulatory network are conserved, while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species.

Discovery of Transcription Factors Novel to Mouse Cerebellar Granule Cell Development Through Laser-Capture Microdissection

Laser-capture microdissection was used to isolate external germinal layer tissue from three developmental periods of mouse cerebellar development: embryonic days 13, 15, and 18. The cerebellar granule cell-enriched mRNA library was generated with next-generation sequencing using the Helicos technology. Our objective was to discover transcriptional regulators that could be important for the development of cerebellar granule cells-the most numerous neuron in the central nervous system. Through differential expression analysis, we have identified 82 differentially expressed transcription factors (TFs) from a total of 1311 differentially expressed genes. In addition, with TF-binding sequence analysis, we have identified 46 TF candidates that could be key regulators responsible for the variation in the granule cell transcriptome between developmental stages. Altogether, we identified 125 potential TFs (82 from differential expression analysis, 46 from motif analysis with 3 overlaps in the two sets). From this gene set, 37 TFs are considered novel due to the lack of previous knowledge about their roles in cerebellar development. The results from transcriptome-wide analyses were validated with existing online databases, qRT-PCR, and in situ hybridization. This study provides an initial insight into the TFs of cerebellar granule cells that might be important for development and provide valuable information for further functional studies on these transcriptional regulators.

Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs

Although ChIP-seq has become a routine experimental approach for quantitatively characterizing the genome-wide binding of transcription factors (TFs), computational analysis procedures remain far from standardized, making it difficult to compare ChIP-seq results across experiments. In addition, although genome-wide binding patterns must ultimately be determined by local constellations of DNA-binding sites, current analysis is typically limited to identifying enriched motifs in ChIP-seq peaks. Here we present Crunch, a completely automated computational method that performs all ChIP-seq analysis from quality control through read mapping and peak detecting and that integrates comprehensive modeling of the ChIP signal in terms of known and novel binding motifs, quantifying the contribution of each motif and annotating which combinations of motifs explain each binding peak. By applying Crunch to 128 data sets from the ENCODE Project, we show that Crunch outperforms current peak finders and find that TFs naturally separate into "solitary TFs," for which a single motif explains the ChIP-peaks, and "cobinding TFs," for which multiple motifs co-occur within peaks. Moreover, for most data sets, the motifs that Crunch identified de novo outperform known motifs, and both the set of cobinding motifs and the top motif of solitary TFs are consistent across experiments and cell lines. Crunch is implemented as a web server, enabling standardized analysis of any collection of ChIP-seq data sets by simply uploading raw sequencing data. Results are provided both in a graphical web interface and as downloadable files.

The Mouse Microbiome Is Required for Sex-Specific Diurnal Rhythms of Gene Expression and Metabolism

The circadian clock and associated feeding rhythms have a profound impact on metabolism and the gut microbiome. To what extent microbiota reciprocally affect daily rhythms of physiology in the host remains elusive. Here, we analyzed transcriptome and metabolome profiles of male and female germ-free mice. While mRNA expression of circadian clock genes revealed subtle changes in liver, intestine, and white adipose tissue, germ-free mice showed considerably altered expression of genes associated with rhythmic physiology. Strikingly, the absence of the microbiome attenuated liver sexual dimorphism and sex-specific rhythmicity. The resulting feminization of male and masculinization of female germ-free animals is likely caused by altered sexual development and growth hormone secretion, associated with differential activation of xenobiotic receptors. This defines a novel mechanism by which the microbiome regulates host metabolism.

Unrealistic phylogenetic trees may improve phylogenetic footprinting

Motivation

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.

Results

Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.

Availability and Implementation

The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo

Supplementary information

Supplementary data are available at Bioinformatics online.

A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.

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.

Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors

Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform ‘motif finding’, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT ‘dilogo’ motifs.

Gene regulatory networks are ultimately encoded in constellations of short binding sites in the DNA and RNA that are recognized by regulatory factors such as transcription factors (TFs). For several decades, computational analysis of regulatory networks has relied on a model of TF sequence-specificity, the position-specific weight-matrix (PSWM), that assumes different positions in a binding site contribute independently to the total binding energy of the TF. However, in recent years evidence has been accumulating that, at least for some TFs, this assumption does not hold. Here we present a new model for the sequence-specificity of TFs, the dinucleotide weight tensor (DWT), that takes arbitrary dependencies between positions in binding sites into account and show that it consistently outperforms PSWMs on high-throughput datasets on TF binding. Moreover, in contrast to previous approaches, DWTs are directly derived from first principles within a Bayesian framework, and contain no tunable parameters. This allows them to be easily applied in practice and we make a suite of tools available for computational analysis with DWTs.

Identification of introns harboring functional sequence elements through positional conservation

Many human introns carry out a function, in the sense that they are critical to maintain normal cellular activity. Their identification is fundamental to understanding cellular processes and disease. However, being noncoding elements, such functional introns are poorly predicted based on traditional approaches of sequence and structure conservation. Here, we generated a dataset of human functional introns that carry out different types of functions. We showed that functional introns share common characteristics, such as higher positional conservation along the coding sequence and reduced loss rates, regardless of their specific function. A unique property of the data is that if an intron is unknown to be functional, it still does not mean that it is indeed non-functional. We developed a probabilistic framework that explicitly accounts for this unique property, and predicts which specific human introns are functional. We show that we successfully predict function even when the algorithm is trained on introns with a different type of function. This ability has many implications in studying regulatory networks, gene regulation, the effect of mutations outside exons on human disease, and on our general understanding of intron evolution and their functional exaptation in mammals.

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

Background

Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.

Results

Here, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.

Conclusion

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1495-1) contains supplementary material, which is available to authorized users.

Paracrine cross-talk between skeletal muscle and macrophages in exercise by PGC-1α-controlled BNP

Activation of resident and infiltrating immune cells is a central event in training adaptation and other contexts of skeletal muscle repair and regeneration. A precise orchestration of inflammatory events in muscle fibers and immune cells is required after recurrent contraction-relaxation cycles. However, the mechanistic aspects of this important regulation remain largely unknown. We now demonstrate that besides a dominant role in controlling cellular metabolism, the peroxisome proliferator-activated receptor γ co-activator 1α (PGC-1α) also has a profound effect on cytokine expression in muscle tissue. Muscle PGC-1α expression results in activation of tissue-resident macrophages, at least in part mediated by PGC-1α-dependent B-type natriuretic peptide (BNP) production and secretion. Positive effects of exercise in metabolic diseases and other pathologies associated with chronic inflammation could accordingly involve the PGC-1α-BNP axis and thereby provide novel targets for therapeutic approaches.

SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules

The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods based on HMMs cannot accurately represent the distance between regulatory elements in TRSs and are cumbersome to model the prevailing dependencies between motifs within CRMs. We propose a probabilistic modeling algorithm called SMCis, which builds a more powerful CRM discovery model based on a hidden semi-Markov model. Our model characterizes the regulatory structure of CRMs and effectively models dependencies between motifs at a higher level of abstraction based on segments rather than nucleotides. Experimental results on three benchmark datasets indicate that our method performs better than the compared algorithms.

The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells

The peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) coordinates the transcriptional network response to promote an improved endurance capacity in skeletal muscle, eg, by coactivating the estrogen-related receptor-α (ERRα) in the regulation of oxidative substrate metabolism. Despite a close functional relationship, the interaction between these 2 proteins has not been studied on a genomic level. We now mapped the genome-wide binding of ERRα to DNA in a skeletal muscle cell line with elevated PGC-1α and linked the DNA recruitment to global PGC-1α target gene regulation. We found that, surprisingly, ERRα coactivation by PGC-1α is only observed in the minority of all PGC-1α recruitment sites. Nevertheless, a majority of PGC-1α target gene expression is dependent on ERRα. Intriguingly, the interaction between these 2 proteins is controlled by the genomic context of response elements, in particular the relative GC and CpG content, monomeric and dimeric repeat-binding site configuration for ERRα, and adjacent recruitment of the transcription factor specificity protein 1. These findings thus not only reveal a novel insight into the regulatory network underlying muscle cell plasticity but also strongly link the genomic context of DNA-response elements to control transcription factor-coregulator interactions.

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.

Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information

BACKGROUND

Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. ChIP-seq has become the major technology to uncover genomic regions containing those binding sites, but motifs predicted by traditional computational approaches using these data are distorted by a ubiquitous binding-affinity bias. Here, we present an approach for detecting and correcting this bias using inter-species information.

RESULTS

We find that the binding-affinity bias caused by the ChIP-seq experiment in the reference species is stronger than the indirect binding-affinity bias in orthologous regions from phylogenetically related species. We use this difference to develop a phylogenetic footprinting model that is capable of detecting and correcting the binding-affinity bias. We find that this model improves motif prediction and that the corrected motifs are typically softer than those predicted by traditional approaches.

CONCLUSIONS

These findings indicate that motifs published in databases and in the literature are artificially sharpened compared to the native motifs. These findings also indicate that our current understanding of transcriptional gene regulation might be blurred, but that it is possible to advance this understanding by taking into account inter-species information available today and even more in the future.

Skeletal muscle PGC-1α modulates systemic ketone body homeostasis and ameliorates diabetic hyperketonemia in mice

Ketone bodies (KBs) are crucial energy substrates during states of low carbohydrate availability. However, an aberrant regulation of KB homeostasis can lead to complications such as diabetic ketoacidosis. Exercise and diabetes affect systemic KB homeostasis, but the regulation of KB metabolism is still enigmatic. In our study in mice with either knockout or overexpression of the peroxisome proliferator-activated receptor-γ coactivator (PGC)-1α in skeletal muscle, PGC-1α regulated ketolytic gene transcription in muscle. Furthermore, KB homeostasis of these mice was investigated during withholding of food, exercise, and ketogenic diet feeding, and after streptozotocin injection. In response to these ketogenic stimuli, modulation of PGC-1α levels in muscle affected systemic KB homeostasis. Moreover, the data demonstrate that skeletal muscle PGC-1α is necessary for the enhanced ketolytic capacity in response to exercise training and overexpression of PGC-1α in muscle enhances systemic ketolytic capacity and is sufficient to ameliorate diabetic hyperketonemia in mice. In cultured myotubes, the transcription factor estrogen-related receptor-α was a partner of PGC-1α in the regulation of ketolytic gene transcription. These results demonstrate a central role of skeletal muscle PGC-1α in the transcriptional regulation of systemic ketolytic capacity.-Svensson, K., Albert, V., Cardel, B., Salatino, S., Handschin, C. Skeletal muscle PGC-1α modulates systemic ketone body homeostasis and ameliorates diabetic hyperketonemia in mice.

SIB Swiss Institute of Bioinformatics Members. The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

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.

ARMADA: Using motif activity dynamics to infer gene regulatory networks from gene expression data

Analysis of gene expression data remains one of the most promising avenues toward reconstructing genome-wide gene regulatory networks. However, the large dimensionality of the problem prohibits the fitting of explicit dynamical models of gene regulatory networks, whereas machine learning methods for dimensionality reduction such as clustering or principal component analysis typically fail to provide mechanistic interpretations of the reduced descriptions. To address this, we recently developed a general methodology called motif activity response analysis (MARA) that, by modeling gene expression patterns in terms of the activities of concrete regulators, accomplishes dramatic dimensionality reduction while retaining mechanistic biological interpretations of its predictions (Balwierz, 2014). Here we extend MARA by presenting ARMADA, which models the activity dynamics of regulators across a time course, and infers the causal interactions between the regulators that drive the dynamics of their activities across time. We have implemented ARMADA as part of our ISMARA webserver, ismara.unibas.ch, allowing any researcher to automatically apply it to any gene expression time course. To illustrate the method, we apply ARMADA to a time course of human umbilical vein endothelial cells treated with TNF. Remarkably, ARMADA is able to reproduce the complex observed motif activity dynamics using a relatively small set of interactions between the key regulators in this system. In addition, we show that ARMADA successfully infers many of the key regulatory interactions known to drive this inflammatory response and discuss several novel interactions that ARMADA predicts. In combination with ISMARA, ARMADA provides a powerful approach to generating plausible hypotheses for the key interactions between regulators that control gene expression in any system for which time course measurements are available.

VKORC1 and VKORC1L1: Why do Vertebrates Have Two Vitamin K 2,3-Epoxide Reductases?

Among all cellular life on earth, with the exception of yeasts, fungi, and some prokaryotes, VKOR family homologs are ubiquitously encoded in nuclear genomes, suggesting ancient and important biological roles for these enzymes. Despite single gene and whole genome duplications on the largest evolutionary timescales, and the fact that most gene duplications eventually result in loss of one copy, it is surprising that all jawed vertebrates (gnathostomes) have retained two paralogous VKOR genes. Both VKOR paralogs function as entry points for nutritionally acquired and recycled K vitamers in the vitamin K cycle. Here we present phylogenetic evidence that the human paralogs likely arose earlier than gnathostomes, possibly in the ancestor of crown chordates. We ask why gnathostomes have maintained these paralogs throughout evolution and present a current summary of what we know. In particular, we look to published studies about tissue- and developmental stage-specific expression, enzymatic function, phylogeny, biological roles and associated pathways that together suggest subfunctionalization as a major influence in evolutionary fixation of both paralogs. Additionally, we investigate on what evolutionary timescale the paralogs arose and under what circumstances in order to gain insight into the biological raison d’être for both VKOR paralogs in gnathostomes.

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm

Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.

The role of master regulators in gene regulatory networks

Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. Arguably, one of the most important of such coordinated responses is the one of amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators, MRs) lead to a large series of transcriptional activation events. This is so since master regulators are transcription factors controlling the expression of other transcription factor molecules and so on. MRs hold a central position related to transcriptional dynamics and control of gene regulatory networks and are often involved in complex feedback and feedforward loops inducing non-trivial dynamics. Recent studies have pointed out to the myocyte enhancing factor 2C (MEF2C, also known as MADS box transcription enhancer factor 2, polypeptide C) as being one of such master regulators involved in the pathogenesis of primary breast cancer. In this work, we perform an integrative genomic analysis of the transcriptional regulation activity of MEF2C and its target genes to evaluate to what extent are these molecules inducing collective responses leading to gene expression deregulation and carcinogenesis. We also analyzed a number of induced dynamic responses, in particular those associated with transcriptional bursts, and nonlinear cascading to evaluate the influence they may have in malignant phenotypes and cancer.Received: 20 Novembre 2014,  Accepted: 24 June 2015; Edited by: C. A. Condat, G. J. Sibona; DOI: http://dx.doi.org/10.4279/PIP.070011Cite as: E Hernández-Lemus, K Baca-López, R Lemus, R García-Herrera, Papers in Physics 7, 070011 (2015)This paper, by E Hernández-Lemus, K Baca-López, R Lemus, R García-Herrera, is licensed under the Creative Commons Attribution License 3.0. 

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.

CAGE-defined promoter regions of the genes implicated in Rett Syndrome

Background

Mutations in three functionally diverse genes cause Rett Syndrome. Although the functions of Forkhead box G1 (FOXG1), Methyl CpG binding protein 2 (MECP2) and Cyclin-dependent kinase-like 5 (CDKL5) have been studied individually, not much is known about their relation to each other with respect to expression levels and regulatory regions. Here we analyzed data from hundreds of mouse and human samples included in the FANTOM5 project, to identify transcript initiation sites, expression levels, expression correlations and regulatory regions of the three genes.

Results

Our investigations reveal the predominantly used transcription start sites (TSSs) for each gene including novel transcription start sites for FOXG1. We show that FOXG1 expression is poorly correlated with the expression of MECP2 and CDKL5. We identify promoter shapes for each TSS, the predicted location of enhancers for each gene and the common transcription factors likely to regulate the three genes. Our data imply Polycomb Repressive Complex 2 (PRC2) mediated silencing of Foxg1 in cerebellum.

Conclusions

Our analyses provide a comprehensive picture of the regulatory regions of the three genes involved in Rett Syndrome.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1177) contains supplementary material, which is available to authorized users.

Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program

Skeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here, we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1α and gene expression upon PGC-1α overexpression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto-underestimated number of transcription factor partners involved in mediating PGC-1α action. In particular, principal component analysis of TFBSs at PGC-1α binding regions predicts that, besides the well-known role of the estrogen-related receptor α (ERRα), the activator protein 1 complex (AP-1) plays a major role in regulating the PGC-1α-controlled gene program of the hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1α.

Transcriptional profiling of the human fibrillin/LTBP gene family, key regulators of mesenchymal cell functions

The fibrillins and latent transforming growth factor binding proteins (LTBPs) form a superfamily of extracellular matrix (ECM) proteins characterized by the presence of a unique domain, the 8-cysteine transforming growth factor beta (TGFβ) binding domain. These proteins are involved in the structure of the extracellular matrix and controlling the bioavailability of TGFβ family members. Genes encoding these proteins show differential expression in mesenchymal cell types which synthesize the extracellular matrix. We have investigated the promoter regions of the seven gene family members using the FANTOM5 CAGE database for human. While the protein and nucleotide sequences show considerable sequence similarity, the promoter regions were quite diverse. Most genes had a single predominant transcription start site region but LTBP1 and LTBP4 had two regions initiating different transcripts. Most of the family members were expressed in a range of mesenchymal and other cell types, often associated with use of alternative promoters or transcription start sites within a promoter in different cell types. FBN3 was the lowest expressed gene, and was found only in embryonic and fetal tissues. The different promoters for one gene were more similar to each other in expression than to promoters of the other family members. Notably expression of all 22 LTBP2 promoters was tightly correlated and quite distinct from all other family members. We located candidate enhancer regions likely to be involved in expression of the genes. Each gene was associated with a unique subset of transcription factors across multiple promoters although several motifs including MAZ, SP1, GTF2I and KLF4 showed overrepresentation across the gene family. FBN1 and FBN2, which had similar expression patterns, were regulated by different transcription factors. This study highlights the role of alternative transcription start sites in regulating the tissue specificity of closely related genes and suggests that this important class of extracellular matrix proteins is subject to subtle regulatory variations that explain the differential roles of members of this gene family.

A penalized Bayesian approach to predicting sparse protein-DNA binding landscapes

MOTIVATION

Cellular processes are controlled, directly or indirectly, by the binding of hundreds of different DNA binding factors (DBFs) to the genome. One key to deeper understanding of the cell is discovering where, when and how strongly these DBFs bind to the DNA sequence. Direct measurement of DBF binding sites (BSs; e.g. through ChIP-Chip or ChIP-Seq experiments) is expensive, noisy and not available for every DBF in every cell type. Naive and most existing computational approaches to detecting which DBFs bind in a set of genomic regions of interest often perform poorly, due to the high false discovery rates and restrictive requirements for prior knowledge.

RESULTS

We develop SparScape, a penalized Bayesian method for identifying DBFs active in the considered regions and predicting a joint probabilistic binding landscape. Using a sparsity-inducing penalization, SparScape is able to select a small subset of DBFs with enriched BSs in a set of DNA sequences from a much larger candidate set. This substantially reduces the false positives in prediction of BSs. Analysis of ChIP-Seq data in mouse embryonic stem cells and simulated data show that SparScape dramatically outperforms the naive motif scanning method and the comparable computational approaches in terms of DBF identification and BS prediction.

AVAILABILITY AND IMPLEMENTATION

SparScape is implemented in C++ with OpenMP (optional at compilation) and is freely available at 'www.stat.ucla.edu/∼zhou/Software.html' for academic use.

ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs

Accurate reconstruction of the regulatory networks that control gene expression is one of the key current challenges in molecular biology. Although gene expression and chromatin state dynamics are ultimately encoded by constellations of binding sites recognized by regulators such as transcriptions factors (TFs) and microRNAs (miRNAs), our understanding of this regulatory code and its context-dependent read-out remains very limited. Given that there are thousands of potential regulators in mammals, it is not practical to use direct experimentation to identify which of these play a key role for a particular system of interest. We developed a methodology that models gene expression or chromatin modifications in terms of genome-wide predictions of regulatory sites and completely automated it into a web-based tool called ISMARA (Integrated System for Motif Activity Response Analysis). Given only gene expression or chromatin state data across a set of samples as input, ISMARA identifies the key TFs and miRNAs driving expression/chromatin changes and makes detailed predictions regarding their regulatory roles. These include predicted activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators. Applying ISMARA to data sets from well-studied systems, we show that it consistently identifies known key regulators ab initio. We also present a number of novel predictions including regulatory interactions in innate immunity, a master regulator of mucociliary differentiation, TFs consistently disregulated in cancer, and TFs that mediate specific chromatin modifications.

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis.