• DNA sequence analysis
• artificial intelligence
• computational biology
• computational genomics
• deep learning

• ADAR
• Alternative splicing
• Breast cancer
• Cancer subtypes
• Cancer transcriptomics
• Long-read sequencing
• Polymorphic insertions
• RNA editing
• Transposable elements
• Tumor heterogeneity

• R00 GM120453 NIGMS NIH HHS
• Startup Funds University of Connecticut Health Center
• Startup Funds The Jackson Laboratory
• R00GM120453 NIGMS NIH HHS
• National Institute of General Medical Sciences

• MicroRNAs/genetics
• MicroRNAs/metabolism
• Deep Learning
• Humans
• Transcription Initiation Site
• Animals
• Mice
• Software
• Gene Expression Regulation
• Transcription Factors/metabolism
• Transcription Factors/genetics

• 32070674 National Natural Science Foundation of China
• JCYJ20220530143615035 Shenzhen Science and Technology Innovation Program
• 2024A0505050001 Guangdong S&T programme
• LGKCSDPT2024001 Warshel Institute for Computational Biology funding from Shenzhen City and Longgang District
• HZQB-KCZYB-2020056 Shenzhen-Hong Kong Cooperation Zone for Technology and Innovation
• 2021E0005 Guangdong Young Scholar Development Fund of Shenzhen Ganghong Group Co., Ltd.
• Guangdong S&T programme

• R00 GM120453 NIGMS NIH HHS
• R35 GM133600 NIGMS NIH HHS

• Brucella
• F-box protein 22
• NF-κB
• brucellosis
• host cell invasion
• inflammation
• macrophages
• phagocytic receptor
• protein degradation
• ubiquitination

• Anti-Inflammatory Agents/metabolism
• Brucella/metabolism
• Brucellosis/microbiology
• Cytokines/metabolism
• Delayed-Action Preparations/metabolism
• F-Box Proteins/genetics
• F-Box Proteins/metabolism
• Humans
• Macrophages
• NF-kappa B/metabolism
• RNA, Small Interfering/genetics
• RNA, Small Interfering/metabolism
• Receptors, Cytoplasmic and Nuclear/metabolism

• Council of Scientific and Industrial Research, India (CSIR)
• Department of Biotechnology, Ministry of Science and Technology, India (DBT)

• Binary encoding
• Distance transformation
• Extra trees
• K-mer
• Promoters

• CpG island
• DNA methylation
• cancer
• ckα
• promoter

• Choline Kinase/genetics
• CpG Islands
• DNA Methylation
• Gene Expression Regulation, Neoplastic
• Humans
• MCF-7 Cells
• Mutation
• Promoter Regions, Genetic

• Partition function
• Position-correlation weight matrix (PCWM)
• Promoter recognition
• Statistical physics model

• DNA, Bacterial/chemistry
• DNA, Bacterial/genetics
• Escherichia coli
• Promoter Regions, Genetic
• Sequence Analysis, DNA/methods
• Software
• Thermodynamics

• Bioinformatics
• Gene regulation
• In silico approaches
• Synthetic promoter
• cis motifs

Components of fish roe possess antioxidant and antiaging activities, making them potentially very beneficial natural resources. Here, we investigated chum salmon eggs (CSEs) as a source of active ingredients, including vitamins, unsaturated fatty acids, and proteins. We incubated human dermal fibroblast cultures for 48 hours with high and low concentrations of CSE extracts and analyzed changes in gene expression. Cells treated with CSE extract showed concentration-dependent upregulation of collagen type I genes and of multiple antioxidative genes, including OXR1, TXNRD1, and PRDX family genes. We further conducted in silico phylogenetic footprinting analysis of promoter regions. These results suggested that transcription factors such as acute myeloid leukemia-1a and cyclic adenosine monophosphate response element-binding protein may be involved in the observed upregulation of antioxidative genes. Our results support the idea that CSEs are strong candidate sources of antioxidant materials and cosmeceutically effective ingredients.

• antiaging
• antioxidative gene
• fish egg
• gene expression analysis
• phylogenetic footprinting analysis

The Wilms’ tumor gene WT1 consists of 10 exons and encodes a zinc finger transcription factor. There are four major WT1 isoforms resulting from alternative splicing at two sites, exon 5 (17AA) and exon 9 (KTS). All major WT1 isoforms are overexpressed in leukemia and solid tumors and play oncogenic roles such as inhibition of apoptosis, and promotion of cell proliferation, migration and invasion. In the present study, a novel alternatively spliced WT1 isoform that had an extended exon 4 (designated as exon 4a) with an additional 153 bp (designated as 4a sequence) at the 3’ end was identified and designated as an Ex4a(+)WT1 isoform. The insertion of exon 4a resulted in the introduction of premature translational stop codons in the reading frame in exon 4a and production of C-terminal truncated WT1 proteins lacking zinc finger DNA-binding domain. Overexpression of the truncated Ex4a(+)WT1 isoform inhibited the major WT1-mediated transcriptional activation of anti-apoptotic Bcl-xL gene promoter and induced mitochondrial damage and apoptosis. Conversely, suppression of the Ex4a(+)WT1 isoform by Ex4a-specific siRNA attenuated apoptosis. These results indicated that the Ex4a(+)WT1 isoform exerted dominant negative effects on anti-apoptotic function of major WT1 isoforms. Ex4a(+)WT1 isoform was endogenously expressed as a minor isoform in myeloid leukemia and solid tumor cells and increased regardless of decrease in major WT1 isoforms during apoptosis, suggesting the dominant negative effects on anti-apoptotic function of major WT1 isoforms. These results indicated that Ex4a(+)WT1 isoform had an important physiological function that regulated oncogenic function of major WT1 isoforms.

• CRISPR-Associated Proteins/genetics
• CRISPR-Associated Proteins/metabolism
• Clustered Regularly Interspaced Short Palindromic Repeats/genetics
• Genetic Vectors/metabolism
• HEK293 Cells
• Humans
• RNA, Guide, CRISPR-Cas Systems/metabolism
• RNA, Messenger/metabolism
• Real-Time Polymerase Chain Reaction
• Transcription Initiation Site
• Tumor Suppressor Protein p53/genetics
• Tumor Suppressor Protein p53/metabolism

• R21 AG040360 NIA NIH HHS
• S10RR025518-01 NCRR NIH HHS
• 5R21AG040360-02 NIA NIH HHS
• S10 RR027431 NCRR NIH HHS
• S10RR027431-01 NCRR NIH HHS
• S10 RR025518 NCRR NIH HHS

We present an update of EPDNew (http://epd.vital-it.ch), a recently introduced new part of the Eukaryotic Promoter Database (EPD) which has been described in more detail in a previous NAR Database Issue. EPD is an old database of experimentally characterized eukaryotic POL II promoters, which are conceptually defined as transcription initiation sites or regions. EPDnew is a collection of automatically compiled, organism-specific promoter lists complementing the old corpus of manually compiled promoter entries of EPD. This new part is exclusively derived from next generation sequencing data from high-throughput promoter mapping experiments. We report on the recent growth of EPDnew, its extension to additional model organisms and its improved integration with other bioinformatics resources developed by our group, in particular the Signal Search Analysis and ChIP-Seq web servers.

• 3T3 Cells
• Adaptor Proteins, Signal Transducing
• Animals
• Base Sequence
• Blotting, Western
• Chemokines
• Eye Proteins/metabolism
• Gene Expression Regulation/physiology
• Gene Knockdown Techniques
• Glycogen Synthase Kinase 3/metabolism
• Glycogen Synthase Kinase 3 beta
• Homeodomain Proteins/metabolism
• Humans
• Intercellular Signaling Peptides and Proteins/genetics
• Intercellular Signaling Peptides and Proteins/metabolism
• Mice
• Molecular Sequence Data
• PAX6 Transcription Factor
• Paired Box Transcription Factors/metabolism
• Promoter Regions, Genetic/genetics
• Protein Isoforms/metabolism
• Repressor Proteins/metabolism
• Reverse Transcriptase Polymerase Chain Reaction
• Sequence Alignment
• Wnt Signaling Pathway/physiology
• beta Catenin/metabolism

• Alternative Splicing
• Animals
• Base Sequence
• Biological Evolution
• Body Patterning/genetics
• Cats
• Cell Proliferation
• Cerebral Cortex/anatomy & histology
• Cerebral Cortex/cytology
• Cerebral Cortex/embryology
• Codon, Nonsense
• Frontal Lobe/anatomy & histology
• Frontal Lobe/cytology
• Frontal Lobe/embryology
• Genetic Variation
• Haplotypes
• Humans
• Mice
• Molecular Sequence Data
• Neural Stem Cells/cytology
• Neural Stem Cells/physiology
• Pedigree
• Promoter Regions, Genetic/genetics
• Receptors, G-Protein-Coupled/genetics
• Sequence Deletion

• U01MH081896 NIMH NIH HHS
• N01-HD-9-0011 NICHD NIH HHS
• HHSN275200900011C NICHD NIH HHS
• MC_PC_15004 Medical Research Council
• GR082557 Wellcome Trust
• P30 HD018655 NICHD NIH HHS
• 2R01NS035129 NINDS NIH HHS
• U01 MH081896 NIMH NIH HHS
• R01 NS035129 NINDS NIH HHS
• Wellcome Trust
• G0700089 Medical Research Council
• Howard Hughes Medical Institute

• Cell Line
• Cell Line, Tumor
• DNA Methylation
• DNA-Binding Proteins/genetics
• DNA-Binding Proteins/metabolism
• Epigenesis, Genetic
• Gene Expression Regulation, Neoplastic
• Humans
• Neoplasms/genetics
• Neoplasms/metabolism
• Organic Anion Transporters, Sodium-Independent/genetics
• Organic Anion Transporters, Sodium-Independent/metabolism
• RNA Interference
• RNA, Messenger/genetics
• Solute Carrier Organic Anion Transporter Family Member 1B3

• Cell Line, Tumor
• Computational Biology/methods
• DNA-Binding Proteins/genetics
• DNA-Binding Proteins/metabolism
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Knockdown Techniques
• Humans
• NF-E2-Related Factor 1/metabolism
• Neurites/metabolism
• Neuroblastoma/genetics
• Neuroblastoma/metabolism
• Promoter Regions, Genetic
• Protein Binding
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Response Elements
• Transcription Factors/genetics
• Transcription Factors/metabolism

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.

Although some preliminary work has revealed the potential transcriptional regulatory function of the introns in eukaryotes, additional evidences are needed to support this conjecture. In this study, we perform systemic analyses of the sequence characteristics of human introns. The results show that the first introns are generally longer and C, G and their dinucleotide compositions are over-represented relative to other introns, which are consistent with the previous findings. In addition, some new phenomena concerned with transcriptional regulation are found: i) the first introns are enriched in CpG islands; and ii) the percentages of the first introns containing TATA, CAAT and GC boxes are relatively higher than other position introns. The similar features of introns are observed in tissue-specific genes. The results further support that the first introns of human genes are likely to be involved in transcriptional regulation, and give an insight into the transcriptional regulatory regions of genes.

• Base Sequence
• CpG Islands
• Databases, Genetic
• Gene Expression Regulation
• Genes/genetics
• Humans
• Introns/genetics
• Nucleotides/genetics
• Regulatory Sequences, Nucleic Acid/genetics
• Repetitive Sequences, Nucleic Acid/genetics
• Transcription, Genetic

Deficiency of the transcription factor MafB, which is normally expressed in macrophages, can underlie cellular dysfunction associated with a range of autoimmune diseases and arteriosclerosis. MafB has important roles in cell differentiation and regulation of target gene expression; however, the mechanisms of this regulation and the identities of other transcription factors with which MafB interacts remain uncertain. Bioinformatics methods provide a valuable approach for elucidating the nature of these interactions with transcriptional regulatory elements from a large number of DNA sequences. In particular, identification of patterns of co-occurrence of regulatory cis-elements (motifs) offers a robust approach.

Here, the directional relationships among several functional motifs were evaluated using the Log-linear Graphical Model (LGM) after extraction and search for evolutionarily conserved motifs. This analysis highlighted GATA-1 motifs and 5’AT-rich half Maf recognition elements (MAREs) in promoter regions of 18 genes that were down-regulated in Mafb deficient macrophages. GATA-1 motifs and MafB motifs could regulate expression of these genes in both a negative and positive manner, respectively. The validity of this conclusion was tested with data from a luciferase assay that used a C1qa promoter construct carrying both the GATA-1 motifs and MAREs. GATA-1 was found to inhibit the activity of the C1qa promoter with the GATA-1 motifs and MafB motifs.

These observations suggest that both the GATA-1 motifs and MafB motifs are important for lineage specific expression of C1qa. In addition, these findings show that analysis of combinations of evolutionarily conserved motifs can be successfully used to identify patterns of gene regulation.

Kindlin-2 is a recently identified FERM and PH domain containing integrin interacting protein. Kindlin-2 is ubiquitously expressed in normal tissues. So far, much effort has been spent exploring the functional aspects of Kindlin-2. However, the transcriptional regulation of Kindlin-2 has not yet been investigated. In this study we identified and functionally characterized the promoter of the human Kindlin-2 gene. We show that the core promoter of Kindlin-2 is a 39 base pair long GC rich fragment located −122/-83 upstream of the Kindlin-2 transcription start site. Functional characterization of this core promoter region by both in silico as well as in vitro/in vivo analysis shows that the transcription factor SP1 plays an important role in regulation of Kindlin-2 expression.

• Animals
• Epithelial Cells/immunology
• Fetus
• Gene Expression Regulation
• Interferon Type I/immunology
• Mice
• Oligonucleotide Array Sequence Analysis
• Protein Serine-Threonine Kinases/genetics
• Protein Serine-Threonine Kinases/metabolism
• Receptor Activator of Nuclear Factor-kappa B/genetics
• Receptor Activator of Nuclear Factor-kappa B/metabolism
• Receptor, Interferon alpha-beta/genetics
• Self Tolerance/genetics
• Signal Transduction
• Stromal Cells/immunology
• TNF Receptor-Associated Factor 6/genetics
• TNF Receptor-Associated Factor 6/metabolism
• Thymus Gland/immunology
• NF-kappaB-Inducing Kinase

• ap-1 transcription factor
• cbp
• creb
• gene expression
• microarray
• pineal gland
• rat
• fra-2
• rgs gene

• Animals
• CREB-Binding Protein/metabolism
• Circadian Rhythm/genetics
• Fos-Related Antigen-2/genetics
• Gene Expression Regulation/physiology
• Genetic Variation
• Genome
• Promoter Regions, Genetic
• RGS Proteins/genetics
• Rats
• Rats, Transgenic
• Transcription Factor AP-1/genetics

• Wellcome Trust
• Intramural NIH HHS

The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.

We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool.

Supervised learning methods are a useful way to combine predictions from diverse sources.

Transcription of transposable elements interspersed in the genome is controlled by complex interactions between their regulatory elements and host factors. However, the same regulatory elements may be occasionally used for the transcription of host genes. One such example is the human L1 retrotransposon, which contains an antisense promoter (ASP) driving transcription into adjacent genes yielding chimeric transcripts. We have characterized 49 chimeric mRNAs corresponding to sense and antisense strands of human genes. Here we show that L1 ASP is capable of functioning as an alternative promoter, giving rise to a chimeric transcript whose coding region is identical to the ORF of mRNA of the following genes: KIAA1797, CLCN5, and SLCO1A2. Furthermore, in these cases the activity of L1 ASP is tissue-specific and may expand the expression pattern of the respective gene. The activity of L1 ASP is tissue-specific also in cases where L1 ASP produces antisense RNAs complementary to COL11A1 and BOLL mRNAs. Simultaneous assessment of the activity of L1 ASPs in multiple loci revealed the presence of L1 ASP-derived transcripts in all human tissues examined. We also demonstrate that L1 ASP can act as a promoter in vivo and predict that it has a heterogeneous transcription initiation site. Our data suggest that L1 ASP-driven transcription may increase the transcriptional flexibility of several human genes.

• Z99 LM999999 Intramural NIH HHS

• Analysis of Variance
• Animals
• Binding Sites
• Binding, Competitive
• Cluster Analysis
• DNA Transposable Elements/genetics
• Gene Expression Profiling
• Gene Expression Regulation
• Humans
• Nucleosomes/metabolism
• Promoter Regions, Genetic/genetics
• Repetitive Sequences, Nucleic Acid/genetics
• Transcription Initiation Site

• Z99 LM999999 Intramural NIH HHS

Accurate identification of promoter regions and transcription start sites (TSS) in genomic DNA allows for a more complete understanding of the structure of genes and gene regulation within a given genome. Many recently published methods have achieved high identification accuracy of TSS. However, models providing more accurate modeling of promoters and TSS are needed. A novel identification method for identifying transcription start sites that improves the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a metric feature based on oligonucleotide positional frequencies, taking into account the nature of promoters. A radial basis function neural network for identifying transcription start sites (RBF-TSS) is proposed and employed as a classification algorithm. Using non-overlapping chunks (windows) of size 50 and 500 on the human genome, the proposed method achieves an area under the Receiver Operator Characteristic curve (auROC) of 94.75% and 95.08% respectively, providing increased performance over existing TSS prediction methods.

Gene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal (switch-like) distributions. We have previously performed genome-scale identification and annotation of genes with switch-like expression at the transcript level in mouse, using large microarray datasets for healthy tissue, in order to study the cellular pathways and regulatory mechanisms involving this class of genes. We showed that a large population of bimodal mouse genes encoding for cell membrane and extracellular matrix proteins is involved in communication pathways. This study expands on previous results by annotating human bimodal genes, investigating their correspondence to bimodality in mouse orthologs and exploring possible regulatory mechanisms that contribute to bimodality in gene expression in human and mouse.

Fourteen percent of the human genes on the HGU133A array (1847 out of 13076) were identified as bimodal or switch-like. More than 40% were found to have bimodal mouse orthologs. KEGG pathways enriched for bimodal genes included ECM-receptor interaction, focal adhesion, and tight junction, showing strong similarity to the results obtained in mouse. Tissue-specific modes of expression of bimodal genes among brain, heart, and skeletal muscle were common between human and mouse. Promoter analysis revealed a higher than average number of transcription start sites per gene within the set of bimodal genes. Moreover, the bimodal gene set had differentially methylated histones compared to the set of the remaining genes in the genome.

The fact that bimodal genes were enriched within the cell membrane and extracellular environment make these genes as candidates for biomarkers for tissue specificity. The commonality of the important roles bimodal genes play in tissue differentiation in both the human and mouse indicates the potential value of mouse data in providing context for human tissue studies. The regulation motifs enriched in the bimodal gene set (TATA boxes, alternative promoters, methlyation) have known associations with complex diseases, such as cancer, providing further potential for the use of bimodal genes in studying the molecular basis of disease.

• Animals
• Base Sequence
• Binding Sites
• Cell Line
• Conserved Sequence
• CpG Islands/genetics
• DNA Methylation
• Drosophila/cytology
• Gene Expression Regulation
• Humans
• Mice
• Molecular Sequence Data
• Organic Cation Transport Proteins/genetics
• Promoter Regions, Genetic/genetics
• Protein Binding
• Sp1 Transcription Factor/metabolism
• Species Specificity

Circadian rhythm is fundamental in regulating a wide range of cellular, metabolic, physiological, and behavioral activities in mammals. Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past, the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown. Here we try to address these questions by integrating all available circadian microarray data in mammals. We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues. Comparisons across mouse, rat, rhesus macaque, and human showed that the circadian phases of known key circadian genes were delayed for 4–5 hours in rat compared to mouse and 8–12 hours in macaque and human compared to mouse. A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data. We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes. The analysis of the network structure revealed the paths through which light, food, and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals. Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

Circadian rhythm is universally present from unicellular organisms to complex organisms and plays an important role in physiological processes such as the sleep–wake cycle in mammals. The mammalian circadian rhythm presents an excellent system for studying gene regulatory networks as a large number of genes are undergoing circadian oscillation in their expression levels. By integrating all available microarray experiments on circadian rhythm in different tissues and species in mammals, we identified a set of common circadian genes lying in the center of the circadian clock. Significant differences in the circadian oscillation of gene expression among mouse, rat, macaque, and human have been observed that underlie their physiological and behavioral differences. We constructed a gene regulatory network for the mouse circadian rhythm using knockout or mutant microarray data that have previously received little attention. Further analysis revealed not only additional feedback loops in the network contributing to the robustness of the circadian clock but also how environmental factors such as light, food, and heat can entrain the circadian rhythm. Our study provides the first gene regulatory network of the mammalian circadian rhythm at the system level. It is also the first attempt to compare gene regulatory networks of circadian rhythm in different mammalian species.

• CpG islands
• principal component analysis
• promoter recognition
• sequence feature
• transcription start sites