Overlapping protein-coding genes in human genome and their coincidental expression in tissues

Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data

Long non-coding RNAs (lncRNAs) have received attention in recent years for their regulatory roles in diverse biological contexts including cancer, yet large gaps remain in our understanding of their mechanisms and global maps of their targets. In this work, we investigated a basic unanswered question of lncRNA systems biology: to what extent can gene expression variation across individuals be attributed to lncRNA-driven regulation? To answer this, we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene's expression variation using a small set of automatically selected lncRNA regulators. A key aspect of this analysis is that it accounts for confounding effects of transcription factors (TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expression for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs can explain more than 20% of expression variation. We observed 25-50% of the putative regulator lncRNAs to be in 'cis' to, i.e., overlapping or located proximally to the target gene. This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was found to be substantially greater than that of trans-located lncRNAs. Additionally, by including statistical interaction terms involving lncRNA-protein pairs as predictors in our regression models, we identified cases where a lncRNA's regulatory effect depends on the presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-gene regulatory network whose edges are supported by co-expression as well as a plausible mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our work is a first attempt to quantify the extent of gene expression control exerted globally by lncRNAs, especially those located proximally to their regulatory targets, in a specific biological (breast cancer) context. It also marks a first step towards systematic reconstruction of lncRNA regulatory networks, going beyond the current paradigm of co-expression networks, and motivates future analyses assessing the generalizability of our findings to additional biological contexts.

Overlaps Between CDS Regions of Protein-Coding Genes in the Human Genome: A Case Study on the NR1D1-THRA Gene Pair

For several decades, it has been known that a substantial number of genes within human DNA exhibit overlap; however, the biological and evolutionary significance of these overlaps remain poorly understood. This study focused on investigating specific instances of overlap where the overlapping DNA region encompasses the coding DNA sequences (CDSs) of protein-coding genes. The results revealed that proteins encoded by overlapping CDSs exhibit greater disorder than those from nonoverlapping CDSs. Additionally, these DNA regions were identified as GC-rich. This could be partially attributed to the absence of stop codons from two distinct reading frames rather than one. Furthermore, these regions were found to harbour fewer single-nucleotide polymorphism (SNP) sites, possibly due to constraints arising from the overlapping state where mutations could affect two genes simultaneously.While elucidating these properties, the NR1D1-THRA gene pair emerged as an exceptional case with highly structured proteins and a distinctly conserved sequence across eutherian mammals. Both NR1D1 and THRA are nuclear receptors lacking a ligand-binding domain at their C-terminus, which is the region where these gene pairs overlap. The NR1D1 gene is involved in the regulation of circadian rhythm, while the THRA gene encodes a thyroid hormone receptor, and both play crucial roles in various physiological processes. This study suggests that, in addition to their well-established functions, the specifically overlapping CDS regions of these genes may encode protein segments with additional, yet undiscovered, biological roles.

Regulation of Notch1 Signalling by Long Non-Coding RNAs in Cancers and Other Health Disorders

Notch1 signalling plays a multifaceted role in tissue development and homeostasis. Currently, due to the pivotal role of Notch1 signalling, the relationship between NOTCH1 expression and the development of health disorders is being intensively studied. Nevertheless, Notch1 signalling is not only controlled at the transcriptional level but also by a variety of post-translational events. First is the ligand-dependent mechanical activation of NOTCH receptors and then the intracellular crosstalk with other signalling molecules-among those are long non-coding RNAs (lncRNAs). In this review, we provide a detailed overview of the specific role of lncRNAs in the modulation of Notch1 signalling, from expression to activity, and their connection with the development of health disorders, especially cancers.

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

BACKGROUND

Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training.

RESULTS

We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools.

CONCLUSIONS

OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

The Functional Meaning of 5'UTR in Protein-Coding Genes

As it is well known, messenger RNA has many regulatory regions along its sequence length. One of them is the 5' untranslated region (5'UTR), which itself contains many regulatory elements such as upstream ORFs (uORFs), internal ribosome entry sites (IRESs), microRNA binding sites, and structural components involved in the regulation of mRNA stability, pre-mRNA splicing, and translation initiation. Activation of the alternative, more upstream transcription start site leads to an extension of 5'UTR. One of the consequences of 5'UTRs extension may be head-to-head gene overlap. This review describes elements in 5'UTR of protein-coding transcripts and the functional significance of protein-coding genes 5' overlap with implications for transcription, translation, and disease.

Mosaic translation hypothesis: chimeric polypeptides produced via multiple ribosomal frameshifting as a basis for adaptability

How many different proteins can be produced from a single spliced transcript? Genome annotation projects overlook the coding potential of reading frames other than that of the reference open reading frames (refORFs). Recently, alternative open reading frames (altORFs) and their translational products, alternative proteins, have been shown to carry out important functions in various organisms. AltORFs overlapping refORFs or other altORFs in a different reading frame may be involved in one fundamental mechanism so far overlooked. A few years ago, it was proposed that altORFs may act as building blocks for chimeric (mosaic) polypeptides, which are produced via multiple ribosomal frameshifting events from a single mature transcript. We adopt terminology from that earlier discussion and call this mechanism mosaic translation. This way of extracting and combining genetic information may significantly increase proteome diversity. Thus, we hypothesize that this mechanism may have contributed to the flexibility and adaptability of organisms to a variety of environmental conditions. Specialized ribosomes acting as sensors probably played a central role in this process. Importantly, mosaic translation may be the main source of protein diversity in genomes that lack alternative splicing. The idea of mosaic translation is a testable hypothesis, although its direct demonstration is challenging. Should mosaic translation occur, we would currently highly underestimate the complexity of translation mechanisms and thus the proteome.

Strategy for RNA-Seq Experimental Design and Data Analysis

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

Dominant transcript expression profiles of human protein-coding genes interrogated with GTEx dataset

The discovery and quantification of mRNA transcripts using short-read next-generation sequencing (NGS) data is a complicated task. There are far more alternative mRNA transcripts expressed by human genes than can be identified from NGS transcriptome data and various bioinformatic pipelines, while the numbers of annotated human protein-coding genes has gradually declined in recent years. It is essential to learn more about the thorough tissue expression profiles of alternative transcripts in order to obtain their molecular modulations and actual functional significance. In this report, we present a bioinformatic database for interrogating the representative tissue of human protein-coding transcripts. The database allows researchers to visually explore the top-ranked transcript expression profiles in particular tissue types. Most transcripts of protein-coding genes were found to have certain tissue expression patterns. This observation demonstrated that many alternative transcripts were particularly modulated in different cell types. This user-friendly tool visually represents transcript expression profiles in a tissue-specific manner. Identification of tissue specific protein-coding genes and transcripts is a substantial advance towards interpreting their biological functions and further functional genomics studies.

Long RNA-Mediated Chromatin Regulation in Fission Yeast and Mammals

As part of a complex network of genome control, long regulatory RNAs exert significant influences on chromatin dynamics. Understanding how this occurs could illuminate new avenues for disease treatment and lead to new hypotheses that would advance gene regulatory research. Recent studies using the model fission yeast Schizosaccharomyces pombe (S. pombe) and powerful parallel sequencing technologies have provided many insights in this area. This review will give an overview of key findings in S. pombe that relate long RNAs to multiple levels of chromatin regulation: histone modifications, gene neighborhood regulation in cis and higher-order chromosomal ordering. Moreover, we discuss parallels recently found in mammals to help bridge the knowledge gap between the study systems.

Overlapping genes in natural and engineered genomes

Modern genome-scale methods that identify new genes, such as proteogenomics and ribosome profiling, have revealed, to the surprise of many, that overlap in genes, open reading frames and even coding sequences is widespread and functionally integrated into prokaryotic, eukaryotic and viral genomes. In parallel, the constraints that overlapping regions place on genome sequences and their evolution can be harnessed in bioengineering to build more robust synthetic strains and constructs. With a focus on overlapping protein-coding and RNA-coding genes, this Review examines their discovery, topology and biogenesis in the context of their genome biology. We highlight exciting new uses for sequence overlap to control translation, compress synthetic genetic constructs, and protect against mutation.

Non-coding regulatory elements: Potential roles in disease and the case of epilepsy

Non-coding DNA (ncDNA) refers to the portion of the genome that does not code for proteins and accounts for the greatest physical proportion of the human genome. ncDNA includes sequences that are transcribed into RNA molecules, such as ribosomal RNAs (rRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and un-transcribed sequences that have regulatory functions, including gene promoters and enhancers. Variation in non-coding regions of the genome have an established role in human disease, with growing evidence from many areas, including several cancers, Parkinson's disease and autism. Here, we review the features and functions of the regulatory elements that are present in the non-coding genome and the role that these regions have in human disease. We then review the existing research in epilepsy and emphasise the potential value of further exploring non-coding regulatory elements in epilepsy. In addition, we outline the most widely used techniques for recognising regulatory elements throughout the genome, current methodologies for investigating variation and the main challenges associated with research in the field of non-coding DNA.

Exon and intron sharing in opposite direction-an undocumented phenomenon in human genome-between Pou5f1 and Tcf19 genes

BACKGROUND

Overlapping genes share same genomic regions in parallel (sense) or anti-parallel (anti-sense) orientations. These gene pairs seem to occur in all domains of life and are best known from viruses. However, the advantage and biological significance of overlapping genes is still unclear. Expressed sequence tags (ESTs) analysis enabled us to uncover an overlapping gene pair in the human genome.

RESULTS

By using in silico analysis of previous experimental documentations, we reveal a new form of overlapping genes in the human genome, in which two genes found on opposite strands (Pou5f1 and Tcf19), share two exons and one intron enclosed, at the same positions, between OCT4B3 and TCF19-D splice variants.

CONCLUSIONS

This new form of overlapping gene expands our previous perception of splicing events and may shed more light on the complexity of gene regulation in higher organisms. Additional such genes might be detected by ESTs analysis also of other organisms.

siRNA Mediate RNA Interference Concordant with Early On-Target Transient Transcriptional Interference

Exogenous siRNAs are commonly used to regulate endogenous gene expression levels for gene function analysis, genotype–phenotype association studies and for gene therapy. Exogenous siRNAs can target mRNAs within the cytosol as well as nascent RNA transcripts within the nucleus, thus complicating siRNA targeting specificity. To highlight challenges in achieving siRNA target specificity, we targeted an overlapping gene set that we found associated with a familial form of multiple synostosis syndrome type 4 (SYSN4). In the affected family, we found that a previously unknown non-coding gene TOSPEAK/C8orf37AS1 was disrupted and the adjacent gene GDF6 was downregulated. Moreover, a conserved long-range enhancer for GDF6 was found located within TOSPEAK which in turn overlapped another gene which we named SMALLTALK/C8orf37. In fibroblast cell lines, SMALLTALK is transcribed at much higher levels in the opposite (convergent) direction to TOSPEAK. siRNA targeting of SMALLTALK resulted in post transcriptional gene silencing (PTGS/RNAi) of SMALLTALK that peaked at 72 h together with a rapid early increase in the level of both TOSPEAK and GDF6 that peaked and waned after 24 h. These findings indicated the following sequence of events: Firstly, the siRNA designed to target SMALLTALK mRNA for RNAi in the cytosol had also caused an early and transient transcriptional interference of SMALLTALK in the nucleus; Secondly, the resulting interference of SMALLTALK transcription increased the transcription of TOSPEAK; Thirdly, the increased transcription of TOSPEAK increased the transcription of GDF6. These findings have implications for the design and application of RNA and DNA targeting technologies including siRNA and CRISPR. For example, we used siRNA targeting of SMALLTALK to successfully restore GDF6 levels in the gene therapy of SYNS4 family fibroblasts in culture. To confidently apply gene targeting technologies, it is important to first determine the transcriptional interference effects of the targeting reagent and the targeted gene.

Transcription of Endogenous Retrovirus Group K Members and Their Neighboring Genes in Chicken Skeletal Muscle Myoblasts

Skeletal muscle myoblasts are myogenic precursor cells that generate myofibers during muscle development and growth. We recently reported that broiler myoblasts, compared to layer myoblasts, proliferate and differentiate more actively and promptly into myocytes, which corresponds well with the muscle phenotype of broilers. Furthermore, RNA sequencing (RNA-seq) revealed that numerous genes are differentially expressed between layer and broiler myoblasts during myogenic differentiation. Based on the RNA-seq data, we herein report that chicken myoblasts transcribe endogenous retrovirus group K member (ERVK) genes. In total, 16 ERVKs were highly expressed in layer myoblasts and two (termed BrK1 and BrK2) were significantly induced in broiler myoblasts. These transcribed ERVKs had a total of 182 neighboring genes within ±100 kb on the chromosomes, of which 40% were concentrated within ±10 kb of the ERVKs. We further investigated whether the transcription of ERVKs affects the expression of their neighboring genes. BrK1 had two neighboring genes; LOC107052719 was overlapping with BrK1 and downregulated in the broiler myoblasts, and FAM19A2 was upregulated in the broiler myoblasts as well as BrK1. BrK2 had 14 neighboring genes, and only one gene, LOC772243, was differentially expressed between layer and broiler myoblasts. LOC772243 was overlapping with BrK2 and suppressed in the broiler myoblasts. These data indicate that the transcription of ERVKs may impact the expression of their neighboring genes in chicken myoblasts.

Transcriptome-wide expression profiling of Sporothrix schenckii yeast and mycelial forms and the establishment of the Sporothrix Genome DataBase

Sporothrix schenckii is a dimorphic fungus existing as mould in the environment and as yeast in the host. The morphological shift between mycelial/yeast phases is crucial for its virulence, but the transcriptional networks implicated in dimorphic transition are still not fully understood. Here, we report the global transcriptomic differences occurring between mould and yeast phases of S. schenckii, including changes in gene expression profiles associated with these distinct cellular phenotypes. Moreover, we also propose a new genome annotation, which reveals a more complex transcriptional architecture than previously assumed. Using RNA-seq, we identified a total of 17 307 genes, of which 11 217 were classified as protein-encoding genes, whereas 6090 were designated as non-coding RNAs (ncRNAs). Approximately ~71 % of all annotated genes were found to overlap and the different-strand overlapping type was the most common. Gene expression analysis revealed that 8795 genes were differentially regulated among yeast and mould forms. Differential gene expression was also observed for antisense ncRNAs overlapping neighbouring protein-encoding genes. The release of transcriptome-wide data and the establishment of the Sporothrix Genome DataBase (http://sporothrixgenomedatabase.unime.it) represent an important milestone for Sporothrix research, because they provide a strong basis for future studies on the molecular pathways involved in numerous biological processes.

Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset

With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized as transcriptome analysis references, we utilized GTEx data to establish a top-ranked transcript isoform expression data resource for human protein-coding genes. Distinctive tissue specific expression profiles and modulations could be observed for individual top-ranked transcripts of protein-coding genes. Protein-coding transcripts or genes do occupy much higher expression fraction in transcriptome data. In addition, top-ranked transcripts are the dominantly expressed ones in various normal tissues. Intriguingly, some of the top-ranked transcripts are noncoding splicing isoforms, which imply diverse gene regulation mechanisms. Comprehensive investigation on the tissue expression patterns of top-ranked transcript isoforms is crucial. Thus, we established a web tool to examine top-ranked transcript isoforms in various human normal tissue types, which provides concise transcript information and easy-to-use graphical user interfaces. Investigation of top-ranked transcript isoforms would contribute understanding on the functional significance of distinctive alternatively spliced transcript isoforms.